Determination of sleep quality for neurological disorders

ABSTRACT

A device determines values for one or more metrics that indicate the quality of a patient&#39;s sleep based on sensed physiological parameter values. Sleep efficiency, sleep latency, and time spent in deeper sleep states are example sleep quality metrics for which values may be determined. The sleep quality metric values may be used, for example, to evaluate the effectiveness of a therapy delivered to the patient by a medical device. In some embodiments, determined sleep quality metric values are automatically associated with the therapy parameter sets according to which the medical device delivered the therapy when the physiological parameter values were sensed, and used to evaluate the effectiveness of the various therapy parameter sets. The medical device may deliver the therapy to treat a non-respiratory neurological disorder, such as epilepsy, a movement disorder, or a psychological disorder. The therapy may be, for example, deep brain stimulation (DBS) therapy.

This application is a continuation-in-part of U.S. application Ser. No.11/081,811, filed Mar. 16, 2005, which is continuation-in-part of U.S.application Ser. No. 10/826,925, filed Apr. 15, 2004 and issued as U.S.Pat. No. 7,717,848, which claims the benefit of U.S. ProvisionalApplication No. 60/553,783, filed Mar. 16, 2004. This application alsoclaims the benefit of U.S. Provisional Application No. 60/785,678, filedMar. 24, 2006. The entire content of each of these applications isincorporated herein by reference.

TECHNICAL FIELD

The invention relates to medical devices and, more particularly, tomedical devices that monitor physiological parameters.

BACKGROUND

In some cases, an ailment may affect the quality of a patient's sleep.For example, neurological disorders may cause a patient to havedifficulty falling asleep, and may disturb the patient's sleep, e.g.,cause the patient to wake. Further, neurological disorders may cause thepatient to have difficulty achieving deeper sleep states, such as one ormore of the nonrapid eye movement (NREM) sleep states.

Epilepsy is an example of a neurological disorder that may affect sleepquality. In some patients, epileptic seizures may be triggered by sleepor transitions between the sleep states, and may occur more frequentlyduring sleep. Furthermore, the occurrence of seizures may disturb sleep,e.g., wake the patient. Often, epilepsy patients are unaware of theseizures that occur while they sleep, and suffer from the effects ofdisturbed sleep, such as daytime fatigue and concentration problems,without ever knowing why.

Other neurological disorders that may negatively affect patient sleepquality include movement disorders, such as tremor, Parkinson's disease,multiple sclerosis, or spasticity. The uncontrolled movements associatedwith such movement disorders may cause a patient to have difficultyfalling asleep, disturb the patient's sleep, or cause the patient tohave difficulty achieving deeper sleep states. Psychological disorders,such as depression, mania, bipolar disorder, or obsessive-compulsivedisorder, may also similar affect the ability of a patient to sleep, orat least experience quality sleep. In the case of depression, a patientmay “sleep” for long periods of the day, but the sleep is not restful,e.g., includes excessive disturbances and does not include deeper, morerestful sleep states. Further, chronic pain, whether of neurologicalorigin or not, as well as congestive heart failure, gastrointestinaldisorders and incontinence, may disturb sleep or otherwise affect sleepquality.

Drugs are often used to treat neurological disorders. In some cases,neurological disorders are treated via an implantable medical device(IMD), such as an implantable stimulator or drug delivery device. Thetreatments for neurological orders may themselves affect sleep quality.

Further, in some cases, poor sleep quality may increase the symptomsexperienced by a patient due to a neurological disorder. For example,poor sleep quality has been linked to increased pain symptoms in chronicpain patients and increased seizure activity in epileptic patients, andmay also result in increased movement disorder symptoms in movementdisorder patients. Further, poor sleep quality may exacerbate manypsychological disorders, such as depression. The link between poor sleepquality and increased symptoms is not limited to ailments thatnegatively impact sleep quality, such as those listed above.Nonetheless, the condition of a patient with such an ailment mayprogressively worsen when symptoms disturb sleep quality, which in turnincreases the frequency and/or intensity of symptoms.

SUMMARY

In general, the invention is directed to techniques for collectinginformation that relates to the quality of patient sleep via a medicaldevice, such as an implantable medical device (IMD). In particular,values for one or more metrics that indicate the quality of thepatient's sleep are determined based on at least one sensedphysiological parameter signal. In some embodiments, sleep qualityinformation is presented to a user based on the sleep quality metricvalues. A clinician, for example, may use the presented sleep qualityinformation to evaluate the effectiveness of therapy delivered to thepatient by the medical device, to adjust the therapy delivered by themedical device, or to prescribe a therapy not delivered by the medicaldevice in order to improve the quality of the patient's sleep.

In some embodiments, the medical device may deliver the therapy to treata non-respiratory neurological disorder, such as epilepsy, a movementdisorder, or a psychological disorder. As discussed above, examples ofmovement disorders are tremor, Parkinson's disease, multiple sclerosis,or spasticity, and examples of psychological disorders are depression,mania, bipolar disorder, or obsessive-compulsive disorder. The medicaldevice may be implanted or external, and may deliver, for example,electrical stimulation, a therapeutic agent, such as a drug, and/or athermal, e.g., cooling, therapy. In some embodiments, the medical devicemay deliver deep brain stimulation (DBS) therapy to treat anon-respiratory neurological disorder, or other disorder or symptom, andmay by implanted on or recessed into the cranium beneath the scalp.

The medical device that delivers the therapy or a separate monitoringdevice monitors one or more physiological parameter signals. Examplephysiological parameters include activity level, posture, heart rate,electrocardiogram (ECG) morphology, electroencephalogram (EEG)morphology, respiration rate, respiratory volume, blood pressure, bloodoxygen saturation, partial pressure of oxygen within blood, partialpressure of oxygen within cerebrospinal fluid, muscular activity andtone, core temperature, subcutaneous temperature, arterial blood flow,melatonin level within one or more bodily fluids, brain electricalactivity, eye motion, and galvanic skin response. In order to monitorone or more of these parameters, the medical device or monitoring devicemay include, or be coupled to one or more sensors, each of whichgenerates a signal as a function of one or more of these physiologicalparameters.

The medical device or monitoring device may determine a value of one ormore sleep quality metrics based on the one or more monitoredphysiological parameters, and/or the variability of one or more of themonitored physiological parameters. In other embodiments, one or both ofthe medical device or monitoring device records values of the one ormore physiological parameters, and provides the physiological parametervalues to a programming device, such as a clinician programming deviceor a patient programming device, or another computing device. In suchembodiments, the programming or other computing device determines valuesof one or more sleep quality metrics based on the physiologicalparameter values received from the medical device and/or the variabilityof one or more of the physiological parameters. The medical device ormonitoring device may provide the recorded physiological parametervalues to the programming or other computing device in real time, or mayprovide physiological parameter values recorded over a period of time tothe programming or other computing device when interrogated.

Sleep efficiency and sleep latency are example sleep quality metrics forwhich a medical device or programming device may determine values. Sleepefficiency may be measured as the percentage of time while the patientis attempting to sleep that the patient is actually asleep. Sleeplatency may be measured as the amount of time between a first time whenthe patient begins attempting to fall asleep and a second time when thepatient falls asleep, and thereby indicates how long a patient requiresto fall asleep.

The time when the patient begins attempting to fall asleep may bedetermined in a variety of ways. For example, the patient may provide anindication that he or she is trying to fall asleep, e.g., via a patientprogramming device. In other embodiments, the medical device ormonitoring may monitor the activity level of the patient, and the timewhen the patient is attempting to fall asleep may be identified bydetermining whether the patient has remained inactive for a thresholdperiod of time, and identifying the time at which the patient becameinactive. In still other embodiments, the medical device or monitoringdevice may monitor patient posture, and the medical device or aprogramming device may identify the time when the patient is recumbent,e.g., lying down, as the time when the patient is attempting to fallasleep. In these embodiments, the medical device or monitoring devicemay also monitor patient activity, and either the medical device,monitoring device, programming device, or other computing device mayconfirm that the patient is attempting to sleep based on the patient'sactivity level.

As another example, the medical device or monitoring device maydetermine the time at which the patient begins attempting to fall asleepbased on the level of melatonin within one or more bodily fluids, suchas the patient's blood, cerebrospinal fluid (CSF), or interstitialfluid. The medical device or monitoring device may also determine amelatonin level based on metabolites of melatonin located in the salivaor urine of the patient. Melatonin is a hormone secreted by the pinealgland into the bloodstream and the CSF as a function of exposure of theoptic nerve to light, which synchronizes the patient's circadian rhythm.In particular, increased levels of melatonin during evening hours maycause physiological changes in the patient, which, in turn, may causethe patient to attempt to fall asleep. The medical device or monitoringdevice may, for example, detect an increase in the level of melatonin,and estimate the time that the patient will attempt to fall asleep basedon the detection.

The time at which the patient has fallen asleep may be determined basedon the activity level of the patient and/or one or more of the otherphysiological parameters that may be monitored by the medical device asindicated above. For example, a discernable change, e.g., a decrease, inone or more physiological parameters, or the variability of one or morephysiological parameters, may indicate that the patient has fallenasleep. A decrease in respiration rate or respiration rate variability,or heart rate or heart rate variability, as examples, may indicate thata patient is asleep.

In some embodiments, a sleep probability metric value may be determinedbased on a value of a physiological parameter monitored by the medicaldevice. In such embodiments, the sleep probability metric value may becompared to a threshold to identify when the patient has fallen asleep.In some embodiments, a plurality of sleep probability metric values aredetermined based on a value of each of a plurality of physiologicalparameters, the sleep probability values are averaged or otherwisecombined to provide an overall sleep probability metric value, and theoverall sleep probability metric value is compared to a threshold toidentify the time that the patient falls asleep.

Thus, in some embodiments, whether a patient is sleeping may bedetermined based on a statistical combination of two or morephysiological parameters. For example, whether a patient is sleeping maybe determined based on a statistical combination of at least one ofactivity level or posture, with at least one of brain electricalactivity or EEG morphology, and also with core temperatures. Othercombinations of the physiological parameters described herein arecontemplated. A sleep probability metric value may be determined foreach of the physiological parameters based on a current value of theparameter, e.g., by application of an equation or look-up table to thevalue. The sleep probability metric values may be combined, e.g., byaverage or sum, which may be weighted, in order to determine whether thepatient is asleep based on the plurality of physiological parameters.

Other sleep quality metrics that may be determined include total timesleeping per day, the amount or percentage of time sleeping duringnighttime or daytime hours per day, and the number of apnea and/orarousal events per night. In some embodiments, which sleep state thepatient is in, e.g., rapid eye movement (REM), or one of the nonrapideye movement (NREM) states (S1, S2, S3, S4) may be determined based onphysiological parameters monitored by the medical device, such as theEEG signal. The amount of time per day spent in these various sleepstates may also be a sleep quality metric. Because they provide the most“refreshing” type of sleep, the amount of time spent in one or both ofthe S3 and S4 sleep states, in particular, may be determined as a sleepquality metric. In some embodiments, average or median values of one ormore sleep quality metrics over greater periods of time, e.g., a week ora month, may be determined as the value of the sleep quality metric.Further, in embodiments in which values for a plurality of the sleepquality metrics are determined, a value for an overall sleep qualitymetric may be determined based on the values for the plurality ofindividual sleep quality metrics.

As discussed above, in some embodiments, the medical device delivers atherapy. At any given time, the medical device delivers the therapyaccording to a current set of therapy parameters. For example, inembodiments in which the medical device delivers electrical stimulation,a therapy parameter set may include a pulse amplitude, a pulse width, apulse rate, a duty cycle, and an indication of active electrodes.Different therapy parameter sets may be selected, e.g., by the patientvia a programming device or a the medical device according to aschedule, and parameters of one or more therapy parameter sets may beadjusted by the patient to create new therapy parameter sets. In otherwords, over time, the medical device delivers the therapy according to aplurality of therapy parameter sets.

In embodiments in which the medical device determines sleep qualitymetric values, the medical device may identify the current therapyparameter set that was in use when a value of one or more sleep qualitymetrics is collected, and may associate that value with the therapyparameter set. For each available therapy parameter set the medicaldevice may store a representative value of each of one or more sleepquality metrics in a memory with an indication of the therapy programswith which that representative value is associated. A representativevalue of sleep quality metric for a therapy parameter set may be themean or median of collected sleep quality metric values that have beenassociated with that therapy parameter set. In other embodiments inwhich a programming device or other computing device determines sleepquality metric values, the medical device may associate recordedphysiological parameter values with the current therapy parameter set inthe memory.

Further, in embodiments in which a separate monitoring device recordsphysiological parameter values or determines sleep quality metricvalues, the monitoring device may mark recorded physiological parametervalues or sleep quality metric values with a current time in a memory,and the medical device may store an indication of a current therapyparameter set and time in a memory. A programming device or othercomputing device may receive indications of the physiological parametervalues or sleep quality metrics and associated times from the monitoringdevice, and indications of the therapy parameter sets and associatedtimes from the medical device, and may associate the physiologicalparameter values or sleep quality metrics with the therapy parameter setthat was delivered by the medical device when the physiologicalparameter values or sleep quality metrics were collected.

A programming device or other computing device according to theinvention may be capable of wireless communication with the medicaldevice, and may receive sleep quality metric values or recordedphysiological parameter values from the medical device or a separatemonitoring device. In either case, when the computing device eitherreceives or determines sleep quality metric values, the computing devicemay provide sleep quality information to a user based on the sleepquality metric values. For example, the computing device may be apatient programmer, and may provide a message to the patient related tosleep quality. The patient programmer may, for example, suggest that thepatient visit a clinician for prescription of sleep medication or for anadjustment to the therapy delivered by the medical device. As otherexamples, the patient programmer may suggest that the patient increasethe intensity of therapy delivered by the medical device duringnighttime hours relative to previous nights, or select a differenttherapy parameter set for use during sleep than the patient had selectedduring previous nights. Further, the patient programmer may provide amessage that indicates the quality of sleep to the patient to, forexample, provide the patient with an objective indication of whether hisor her sleep quality is good, adequate, or poor.

In other embodiments, the computing device is a clinician programmerthat presents information relating to the quality of the patient's sleepto a clinician. The clinician programmer may present, for example, atrend diagram of values of one or more sleep quality metrics over time.As other examples, the clinician programmer may present a histogram orpie chart illustrating percentages of time that a sleep quality metricwas within various value ranges.

As indicated above, the computing device may receive representativevalues for one or more sleep quality metrics or the physiologicalparameter values from the therapy delivering medical device or separatemonitoring device. The computing device may receive informationidentifying the therapy parameter set with which the representativevalues are associated, or may itself associate received physiologicalparameter or sleep quality metric values with therapy parameter setsbased on time information received from one or more devices. Inembodiments in which the computing device receives physiologicalparameter values, the computing device may determine sleep qualitymetric values associated with the plurality of parameter sets based onthe physiological parameter values, and representative sleep qualitymetric values for each of the therapy parameter sets based on the sleepquality metric values associated with the therapy parameter sets. Insome embodiments, the computing device may determine the variability ofone or more of the physiological parameters based on the physiologicalparameter values received from the medical device or monitoring device,and may determine sleep quality metric values based on the physiologicalparameter variabilities.

The computing device may display a list of the therapy parameter sets tothe clinician ordered according to their associated representative sleepquality metric values. Such a list may be used by the clinician toidentify effective or ineffective therapy parameter sets. Where aplurality of sleep quality metric values are determined, the programmingdevice may order the list according to values of a user-selected one ofthe sleep quality metrics.

In other embodiments, a system according to the invention does notinclude a programming or other computing device. For example, anexternal medical device according to the invention may include adisplay, determine sleep quality metric values, and display sleepquality information to a user via the display based on the sleep qualitymetric values. Further, any of the devices described herein mayautomatically select or adjust a therapy parameter set based on sleepquality metric values, e.g., select one of a plurality of therapyparameter sets based on the representative sleep quality metric valuesassociated with each of the plurality of therapy parameter sets.

In one embodiment, the invention is directed to a method comprisingdelivering a therapy from a medical device to a patient to treat anon-respiratory neurological disorder of the patient, sensing at leastone physiological parameter signal during treatment of the patient withthe medical device, determining values of a sleep quality metric basedon the at least one physiological parameter signal, and providing thesleep quality metric values to a user for evaluation of the therapy.

In another embodiment, the invention is directed to a medical systemcomprising a medical device that delivers a therapy to a patient totreat a non-respiratory neurological disorder of the patient, and aprocessor that determines values of a sleep quality metric based on atleast one physiological parameter signal sensed during treatment of thepatient with the medical device, and provides the sleep quality metricvalues to a user for evaluation of the therapy.

In another embodiment, the invention is directed to a method comprisingdelivering deep brain stimulation (DBS) therapy from a medical device toa patient via a lead implanted in a brain of the patient, sensing atleast one physiological parameter signal during treatment of the patientwith the medical device, determining values of a sleep quality metricbased on the at least one physiological parameter signal, and providingthe sleep quality metric values to a user for evaluation of the DBStherapy.

In another embodiment, the invention is directed to a medical systemcomprising a lead implanted in a brain of a patient, a medical devicecoupled to the lead that delivers deep brain stimulation (DBS) therapyto the patient via the lead, and a processor that determines values of asleep quality metric based on at least one physiological parametersignal sensed during treatment of the patient with the medical device,and provides the sleep quality metric values to a user for evaluation ofthe DBS therapy.

The invention may be capable of providing one or more advantages. Forexample, by providing information related to the quality of a patient'ssleep to a clinician and/or the patient, a system according to theinvention can improve the course of treatment of a neurological disorderof the patient, such as chronic pain, epileptic seizures, a movementdisorder, or a psychological disorder. Using the sleep qualityinformation provided by the system, the clinician and/or patient can,for example, make changes to the therapy provided by a medical device inorder to better address symptoms which are disturbing the patient'ssleep. Further, a clinician may choose to prescribe a therapy that willimprove the patient's sleep, such as a sleep inducing medication, insituations where poor sleep quality is increasing symptoms experiencedby the patient. In addition, the system may detect which sleep state thepatient is experiencing based upon the EEG signal.

The details of one or more embodiments of the invention are set forth inthe accompanying drawings and the description below. Other features,objects, and advantages of the invention will be apparent from thedescription and drawings, and from the claims.

BRIEF DESCRIPTION OF DRAWINGS

FIG. 1 is a conceptual diagram illustrating an example system thatincludes an implantable medical device implanted in the chest thatcollects sleep quality information.

FIG. 2 is a conceptual diagram illustrating another example system thatincludes an implantable medical device implanted under the scalp thatcollects sleep quality information.

FIG. 3 is a block diagram illustrating the example system andimplantable medical device of FIGS. 1 and 2.

FIG. 4 is a logic diagram illustrating an example circuit that detectsthe sleep state of a patient from the electroencephalogram (EEG) signal.

FIG. 5 is a conceptual diagram illustrating another example system thatincludes an implantable medical device that collects sleep qualityinformation according to the invention.

FIG. 6 is a block diagram further illustrating the example system andimplantable medical device of FIG. 5.

FIG. 7 is a block diagram illustrating an example memory of animplantable medical device that collects sleep quality information.

FIG. 8 is a flow diagram illustrating an example method for collectingsleep quality information that may be employed by an implantable medicaldevice.

FIG. 9 is a flow diagram illustrating an example method for collectingsleep quality information and sleep type that may be employed by animplantable medical device.

FIG. 10 is a flow diagram illustrating an example method for associatingsleep quality information with therapy parameter sets that may beemployed by an implantable medical device.

FIG. 11 is a block diagram illustrating an example clinician programmer.

FIG. 12 is a flow diagram illustrating an example method for presentingsleep quality information to a clinician that may be employed by aclinician programmer.

FIG. 13 illustrates an example list of therapy parameter sets andassociated sleep quality information that may be presented by aclinician programmer.

FIG. 14 is a flow diagram illustrating an example method for displayinga list of therapy parameter sets and associated sleep qualityinformation that may be employed by a clinician programmer.

FIG. 15 is a block diagram illustrating an example patient programmer.

FIG. 16 is a flow diagram illustrating an example method for presentinga sleep quality message to a patient that may be employed by a patientprogrammer.

FIG. 17 is a conceptual diagram illustrating a monitor that monitorsvalues of one or more physiological parameters of the patient insteadof, or in addition to, a therapy delivering medical device.

DETAILED DESCRIPTION

One or more physiological parameters are monitored to identify thequality of a patient's sleep. Some physiological parameters that may bemonitored include activity, posture, heart rate, respiration rate,electrocardiogram (ECG) morphology, subcutaneous or core temperature,muscular tone, electrical activity of a brain of the patient,electroencephalogram (EEG) morphology, or eye motion. In someembodiments, for example, the EEG may be analyzed to detect if thepatient is in the S1, S2, S3, S4, or REM sleep state. This sleep stateinformation may be used to determine the duration of deep sleep for thepatient, which may be indicative of the sleep quality of the patient.However, other physiological parameters such as activity, posture, coreor subcutaneous temperature, or heart rate may be used instead of or inaddition to the EEG when determining whether the patient is asleep, inwhich sleep state the patient is, or the quality of sleep for thepatient in general.

Sleep quality information may take the form of values for one or moresleep quality metrics. A person or device may evaluate or modify atherapy based on sleep quality metric values in an effort to improvetherapy efficacy. In some embodiments, sleep quality metric values maybe associated with the current therapy parameter set that is being usedto deliver the therapy, and the effectiveness of each of a plurality oftherapy parameter sets may be evaluated by reviewing their associatedsleep quality metric values. In some embodiments, an implanted medicaldevice (IMD) may deliver therapy, monitor the one or more physiologicalparameters, determine associated sleep quality metric values, and, insome cases, make changes to the therapy based upon the sleep qualitymetric values. Systems according to the invention may help to improvetherapy effectiveness and overall patient quality of life.

FIG. 1 is a conceptual diagram illustrating an example system 10 thatincludes an implantable medical device (IMD) 18 implanted in the chestof a patient 12. IMD 18 collects information relating to the quality ofsleep experienced by patient 12. Sleep quality information collected byIMD 18 may be provided to a user, such as a clinician or the patient.Using the sleep quality information collected by IMD 18, a currentcourse of therapy for an ailment of patient 12 may be evaluated, and animproved course of therapy for the ailment may be identified. In someembodiments, IMD 18 automatically processes the sleep qualityinformation and autonomously modifies the therapy in an attempt toimprove sleep quality.

In the illustrated example, IMD 18 takes the form of an implantableneurostimulator that delivers neurostimulation therapy in the form ofelectrical pulses to patient 12. However, the invention is not limitedto implementation via an implantable neurostimulator. For example, insome embodiments of the invention, an implantable pump that delivers adrug or other therapeutic agent to brain, intrathecal space, or otherlocations within patient may collect sleep quality information. In thecase of a drug delivery device, a therapy parameter set may determinethe flow rate of delivery and delivery timing for a fluid drug. Asanother example, the invention may be embodied in a device that deliversa thermal therapy, e.g., cooling therapy, to the brain or other tissuesinstead of or in addition to electrical stimulation or a therapeuticagent. In other embodiments, an implantable cardiac rhythm managementdevice, such as a pacemaker, may collect sleep quality information.

Further, the invention is not limited to implementation via an IMD, anda device that collects sleep quality information need not deliver atherapy. In some cases, a system may include a therapy deliveringdevice, and a monitor that collects sleep quality information. In otherwords, any implantable or external medical device, which does or doesnot deliver therapy, may collect sleep quality information according tothe invention.

In the example of FIG. 1, IMD 18 delivers neurostimulation therapy topatient 12 via leads 24 and 26, which are connected to IMD 18 via a leadextension 22. Lead extension 22 couples to IMD 18 via connector 20.Leads 24 and 26 may, as shown in FIG. 1, be implanted within thecerebrum of the brain of patient 12, and IMD 18 may deliver stimulationtherapy to the brain, e.g., deep brain stimulation (DBS). In theillustrated example, leads 24 and 26 are symmetrical or stereotactic,i.e., both leads are implanted at similar locations in each the rightand left hemisphere of brain 16. In this manner, IMD 18 deliversstimulation to bilateral locations within brain 16.

However, the invention is not limited to the configuration of leads 24and 26 or extensions 22 shown in FIG. 1. In other embodiments,non-symmetrical leads or a single lead may be used to deliver DBStherapy. In other words, one or more leads 24 and 26 may be coupleddirectly to IMD 18, or be coupled to IMD 18 by one or more extensions22, and may extend from IMD 18 to any one or more portions of brain 16.

IMD 18 may deliver electrical stimulation to the brain to treat any of avariety of neurological disorders. For example, IMD 18 may deliver DBSin order to, for example, reduce the frequency and severity of epilepticseizures experienced by patient 12. As other examples, IMD 18 maydeliver DBS in order to reduce the symptoms of a movement disorder orpsychological disorder, such as tremor, Parkinson's disease, multiplesclerosis, spasticity, depression, mania, bipolar disorder, orobsessive-compulsive disorder. Additionally, IMD 18 may deliver DBS totreat chronic pain or other non-respiratory neurological disorders,e.g., excluding for example central sleep apnea. Further, IMD 18 maydeliver stimulation to locations other than the brain to treat suchdisorders, or may deliver stimulation to the brain to treat otherdisorders.

Additionally, leads 24 and 26 may be implanted proximate to the spinalcord to treat, for example, chronic pain; on or within the heart totreat any of a variety of cardiac disorders, such as congestive heartfailure or arrhythmia; proximate to the gastrointestinal tract to treatany of a variety of gastrointestinal disorders, such as gastroparesis orconstipation; within the pelvic floor to treat disorders such asincontinence; or proximate to any peripheral nerves to treat any of avariety of disorders, such as peripheral neuropathy or other types ofchronic pain. IMD 18 may deliver either or both of responsive, e.g.,closed-loop, or non-responsive stimulation. An example of responsivestimulation is delivery of DBS in response to detection of electricalactivity within the brain of patient 12 associated with a seizure.

IMD 18 delivers therapy according to a set of therapy parameters, i.e.,a set of values for a number of parameters that define the therapydelivered according to that therapy parameter set. In embodiments whereIMD 18 delivers neurostimulation therapy in the form of electricalpulses, the parameters in each parameter set may include voltage orcurrent pulse amplitudes, pulse widths, pulse rates, and the like.Further, each of leads 24 and 26 includes electrodes disposed at thedistal end of each lead, and a therapy parameter set may includeinformation identifying which electrodes have been selected for deliveryof pulses, and the polarities of the selected electrodes. Therapyparameter sets used by IMD 18 may include parameter sets programmed by aclinician (not shown), and parameter sets representing adjustments madeby patient 12 to these preprogrammed sets. In some embodiments,adjustments or modifications to therapy parameter sets may be performedautomatically or suggested to patient 12 by IMD 18 or other componentsof system 10, such as a programmer.

In the illustrated example, system 10 includes a programmer 28.Programmer 28 may be a clinician or patient programmer that communicateswith IMD 18, and system 10 may include any number of programmers 28which may act as clinician or patient programmers. A clinician (notshown) may use programmer 28 to program therapy for patient 12, e.g.,specify a number of therapy parameter sets and communicate the parametersets to IMD 18. The clinician may also use programmer 28 to retrieveinformation collected by IMD 18. The clinician may use programmer 28 tocommunicate with IMD 18 both during initial programming of IMD 18, andfor collection of information and further programming during follow-upvisits.

Programmer 28 may include a display (not shown) to present informationto the user and an input mechanism (not shown), e.g., a keypad, thatallows the user to interact with the programmer. In some embodiments,the display may be a touch screen display, and a user may interact withprogrammer 28 via the display. A user may also interact with clinicianprogrammer 28 using peripheral pointing devices, such as a stylus ormouse. The keypad may take the form of an alphanumeric keypad or areduced set of keys associated with particular functions. Programmer 28may be embodied similar to clinician programmer 128 or patientprogrammer 134 of FIG. 5. However, programmer 28 is not limited to theembodiments depicted in FIG. 5.

As described above, programmer 28 may be a patient programmer. Patient12 may use programmer 28 to control the delivery of therapy by IMD 18.For example, using programmer 28, patient 12 may select a currenttherapy parameter set from among the therapy parameter setspreprogrammed by the clinician, or may adjust one or more parameters ofa preprogrammed therapy parameter set to arrive at the current therapyparameter set.

Programmer 28 may be any type of computing device. For example,programmer 28 may be a hand-held or tablet-based computing device, adesktop computing device, or a workstation. In addition, programmer 28may be a virtual programmer in that a remote user may communicate withIMD 18 without being in the same room as patient 12.

IMD 18 and programmer 28 communicate via wireless communication.Programmer 28 may communicate via wireless communication with IMD 18using radio frequency (RF) telemetry techniques known in the art.Possible communications may follow RF protocols according to the 802.11or Bluetooth specification sets, infrared communication according to theIRDA specification set, or other standard or proprietary telemetryprotocols.

As mentioned above, IMD 18 collects information relating to the qualityof sleep experienced by patient 12. Specifically, as will be describedin greater detail below, IMD 18 monitors one or more physiologicalparameters of patient 12, and determines values for one or more metricsthat indicate the quality of sleep based on values of the physiologicalparameters. Example physiological parameters that IMD 18 may monitorinclude activity level, posture, heart rate, ECG morphology, respirationrate, respiratory volume, blood pressure, blood oxygen saturation,partial pressure of oxygen within blood, partial pressure of oxygenwithin cerebrospinal fluid (CSF), muscular activity and tone, coretemperature, subcutaneous temperature, arterial blood flow, the level ofmelatonin within one or more bodily fluids, brain electrical activity,electroencephalogram (EEG) morphology, and eye motion. In some externalmedical device embodiments of the invention, galvanic skin response mayadditionally or alternatively be monitored. Further, in someembodiments, IMD 18 additionally or alternatively monitors thevariability of one or more of these parameters. In order to monitor oneor more of these parameters, IMD 18 may include or be coupled to one ormore sensors (not shown in FIG. 1), each of which generates a signal asa function of one or more of these physiological parameters.

For example, IMD 18 may determine sleep efficiency and/or sleep latencyvalues. Sleep efficiency and sleep latency are example sleep qualitymetrics. IMD 18 may measure sleep efficiency as the percentage of timewhile patient 12 is attempting to sleep that patient 12 is actuallyasleep. IMD 18 may measure sleep latency as the amount of time between afirst time when patient 12 begins attempting to fall asleep and a secondtime when patient 12 falls asleep.

IMD 18 may identify the time at which patient 12 begins attempting tofall asleep in a variety of ways. For example, IMD 18 may receive anindication from the patient that the patient is trying to fall asleepvia programmer 28. In other embodiments, IMD 18 may monitor the activitylevel of patient 12, and identify the time when patient 12 is attemptingto fall asleep by determining whether patient 12 has remained inactivefor a threshold period of time, and identifying the time at whichpatient 12 became inactive. In still other embodiments, IMD 18 maymonitor the posture of patient 12, and may identify the time when thepatient 12 becomes recumbent, e.g., lies down, as the time when patient12 is attempting to fall asleep. In these embodiments, IMD 18 may alsomonitor the activity level of patient 12, and confirm that patient 12 isattempting to sleep based on the activity level.

As another example, IMD 18 may determine the time at which patient 12 isattempting to fall asleep based on the level of melatonin within one ormore bodily fluids of patient 12, such as the patient's blood,cerebrospinal fluid (CSF), or interstitial fluid. IMD 18 may alsodetermine a melatonin level based on metabolites of melatonin located inthe saliva or urine of the patient. Melatonin is a hormone secreted bythe pineal gland into the bloodstream and the CSF as a function ofexposure of the optic nerve to light, which synchronizes the patient'scircadian rhythm. In particular, increased levels of melatonin duringevening hours may cause physiological changes in patient 12, which, inturn, may cause patient 12 to attempt to fall asleep.

IMD 18 may, for example, detect an increase in the level of melatonin ina bodily fluid, and estimate the time that patient 12 will attempt tofall asleep based on the detection. For example, IMD 18 may compare themelatonin level or rate of change in the melatonin level to a thresholdlevel, and identify the time that threshold value is exceeded. IMD 18may identify the time that patient 12 is attempting to fall asleep asthe time that the threshold is exceeded, or some amount of time afterthe threshold is exceeded.

IMD 18 may identify the time at which patient 12 has fallen asleep basedon the activity level of the patient and/or one or more of the otherphysiological parameters that may be monitored by IMD 18 as indicatedabove. For example, IMD 18 may identify a discernable change, e.g., adecrease, in one or more physiological parameters, or the variability ofone or more physiological parameters, which may indicate that patient 12has fallen asleep. In some embodiments, IMD 18 determines a sleepprobability metric value based on a value of a physiological parametermonitored by the medical device. In such embodiments, the sleepprobability metric value may be compared to a threshold to identify whenthe patient has fallen asleep. In some embodiments, a sleep probabilitymetric value is determined based on a value of each of a plurality ofphysiological parameters, the sleep probability values are averaged orotherwise combined to provide an overall sleep probability metric value,and the overall sleep probability metric value is compared to athreshold to identify the time that the patient falls asleep.

Other sleep quality metrics include total time sleeping per day, and theamount or percentage of time sleeping during nighttime or daytime hoursper day. In some embodiments, IMD 18 may be able to detect arousalevents during sleep based on one or more monitored physiologicalparameters, and the number of arousal events per night may be determinedas a sleep quality metric. Further, in some embodiments IMD 18 may beable to determine in which sleep state patient 12 is, e.g., rapid eyemovement (REM), S1, S2, S3, or S4, based on the EEG and/or one or moreother monitored physiological parameters. The amount of time per dayspent in these various sleep states may be a sleep quality metric.Detecting certain sleep states may be useful for evaluation of thequality of sleep of patient 12.

For example, the S3 and S4 sleep states may be of particular importanceto the quality of sleep experienced by patient 12. Interruption fromreaching these states, or inadequate time per night spent in thesestates, may cause patient 12 to not feel rested. For this reason, the S3and S4 sleep states are believed to provide the “refreshing” part ofsleep.

In some cases, interruption from reaching the S3 and S4 sleep states, orinadequate time per night spent in these states may increase the numberor severity of epileptic seizures, movement or psychological disordersymptoms, or chronic pain. For this reason, in some embodiments, IMD 18may determine an amount or percentage of time spent in one or both ofthe S3 and S4 sleep states as a sleep quality metric.

In embodiments in which IMD 18 is used to detect and treat epilepticevents, detecting sleep states based on the EEG may be difficult. Thisis because epileptic events may be incorrectly classified as “sleepspindles,” which are present in the EEG during sleep and may be used todetect sleep states. In some embodiments, as will be described below, anEEG signal may be filtered and analyzed, as well as statisticallycombined with other physiological parameters, to minimize thepossibility of falsely detecting sleep or a sleep state.

In some embodiments, IMD 18 may determine average or median values ofone or more sleep quality metrics over greater periods of time, e.g., aweek or a month, as the value of the sleep quality metric. Further, inembodiments in which IMD 18 collects values for a plurality of the sleepquality metrics identified above, IMD 18 may determine a value for anoverall sleep quality metric based on the collected values for theplurality of sleep quality metrics. IMD 18 may determine the value of anoverall sleep quality metric by applying a function or look-up table toa plurality of sleep quality metric values, which may also include theapplication of weighting factors to one or more of the individual sleepquality metric values.

In some embodiments, IMD 18 may identify the current set of therapyparameters when a value of one or more sleep quality metrics iscollected, and may associate that value with the current therapyparameter sets. For example, for each of a plurality therapy parametersets used over time by IMD 18 to deliver therapy to patient 12, IMD 18may store a representative value of each of one or more sleep qualitymetrics in a memory with an indication of the therapy parameter set withwhich that representative value is associated. A representative value ofsleep quality metric for a therapy parameter set may be the mean ormedian of collected sleep quality metric values that have beenassociated with that therapy parameter set.

Programmer 28 may receive sleep quality metric values from IMD 18, andmay provide sleep quality information to a user based on the sleepquality metric values. For example, programmer 28 may provide a messageto patient 12, e.g., via a display, related to sleep quality based onreceived sleep quality metric values. Programmer 28 may, for example,suggest that patient 12 visit a clinician for prescription of sleepmedication or for an adjustment to the therapy delivered by IMD 18. Asother examples, programmer 28 may suggest that patient 12 increase theintensity of therapy delivered by IMD 18 during nighttime hours relativeto previous nights, or select a different therapy parameter set for useby IMD 18 than the patient had selected during previous nights. Further,programmer 28 may report the quality of the patient's sleep to patient12 to provide patient 12 with an objective indication of whether his orher sleep quality is good, adequate, or poor. Programmer 28 may alsopresent a graphical representation of the sleep quality metric values,such as a trend diagram of values of one or more sleep quality metricsover time, or a histogram or pie chart illustrating percentages of timethat a sleep quality metric was within various value ranges.

In embodiments in which IMD 18 associates sleep quality metric valueswith therapy parameter sets, programmer 28 may receive representativevalues for one or more sleep quality metrics from IMD 18 and informationidentifying the therapy parameter sets with which the representativevalues are associated. Using this information, programmer 28 may displaya list of the therapy parameter sets to the clinician ordered accordingto their associated representative sleep quality metric values. Theclinician may use such a list to identify effective or ineffectivetherapy parameter sets. Where a plurality of sleep quality metric valuesare collected, clinician programmer 28 may order the list according tovalues of a user-selected one of the sleep quality metrics. In thismanner, the clinician may quickly identify the therapy parameter setsproducing the best results in terms of sleep quality.

Alternatively, IMD 18 or programmer 28 may present this information topatient 12 or otherwise suggest to the patient whether the therapyparameter set should be changed to improve sleep quality. In otherembodiments, IMD 18 or programmer 28 may autonomously make changes tothe therapy parameter set based on the sleep quality metric values. Forexample, one of a plurality of therapy parameter sets may automaticallybe selected to control delivery of therapy based on the sleep qualitymetric values associated with the therapy parameter sets.

FIG. 2 is a conceptual diagram illustrating another example system 30that includes an IMD 32 that collects sleep quality information. System30 includes IMD 32, connection ports 34 and 36, leads 42 and 44implanted within brain 16, and programmer 28. IMD 32 is substantiallysimilar to IMD 18 of FIG. 1. However, IMD 32 is configured to beimplanted beneath the scalp of head 14. In some embodiments, IMD 32 maybe implanted at least partially within the skull of patient 12, e.g.,within a recess or hole formed in or through the skull. Implanting IMD32 in head 14 of patient 12 may reduce the length of leads 42 and 44 andreduce number of areas that must be surgically altered in the patientfor implantation of the IMD.

Implantation of an IMD in head 14, as illustrated in FIG. 2, is analternative to implantation of an IMD within the chest of the patient,as illustrated in FIG. 1. However, the invention is not limited to theimplantation locations illustrated in FIGS. 1 and 2. An IMD thatcollects sleep quality information according to the invention may beimplanted anywhere within a patient.

Leads 42 and 44 are tunneled from IMD 32 under the scalp of patient 12to the location where each lead enters the skull of patient 12. Similarto leads 24 and 26 of FIG. 1, leads 42 and 44 are symmetrical orstereotactic leads, i.e., both leads are implanted at similar locationsin each the right and left hemisphere of brain 16. In this manner, IMD32 may deliver stimulation to bilateral locations within brain 16. Othertherapies may also be provided via leads 42 and 44 or other leadscoupled to IMD 32. In some embodiments, connection ports 34 and 36 maybe located at a different location on IMD 32 to provide alternativepositions of leads 42 and 44.

IMD 32 may be specifically designed to be implanted in head 14 ofpatient 12. IMD 32 may have a thin profile to minimize the protrusion ofthe IMD away from the skull. IMD 32 may be approximately rectangular inshape; however, the IMD may be closer in shape to a circle, square,oval, trapezoid, or other shape that best fits into the implantationsite. In some embodiments, IMD 32 may include a compliant outercovering, which may at least partially cover or encapsulate a more rigidhousing. IMD 32 may include separated components or multiple smallermodules within such a covering, which may enable IMD 32 to have athinner profile.

As mentioned above with respect to system 10, system 30 may be capableof delivering electrical stimulation therapy and collecting sleepinformation. The sleep information may be collected by monitoring atleast one physiological parameter that may be indicative of the sleepstate of patient 12. IMD 32, similar to IMD 18 of FIG. 1, may determinesleep quality metric values based upon the monitored physiologicalparameters. IMD 32 may internally assign the sleep quality metric valuesto the current therapy parameter set. In alternative embodiments, IMD 32may transmit the sleep quality metric values to programmer 28.Programmer 28 may then assign the sleep quality metric values to thecurrent therapy parameter set. As described in FIG. 1, programmer 28 maybe a clinician or patient programmer.

FIG. 3 is a block diagram illustrating the example system andimplantable medical device of FIGS. 1 and 2. IMD 18 of system 10 isshown as an example in FIG. 3; however, the block diagram may also beapplicable to similar IMD 32 of system 30. FIG. 3 illustrates an exampleconfiguration of IMD 18 and leads 24 and 26. FIG. 3 also illustratessensors 50A and 50B (collectively “sensors 50”) that generate signals asa function of one or more physiological parameters of patient 12. Aswill be described in greater detail below, IMD 18 monitors physiologicalparameter signals received via leads 24, 26 and/or from one or moresensors 50 to determine values for one or more metrics that areindicative of sleep quality.

Some techniques for determining sleep quality metrics include monitoringan EEG signal of patient 12 received via leads 24, 26. For example, insome embodiments, IMD 18 may analyze the EEG signal to identify when thepatient is asleep, or within which sleep state, i.e., S1, S2, S3, S4, orREM sleep state, patient 12 is. As will be described herein, IMD 18 mayfilter the EEG to analyze certain frequencies associated with certainsleep states.

IMD 18 may deliver neurostimulation therapy via electrodes 46A-D of lead24 and electrodes 46E-H of lead 26 (collectively “electrodes 46”).Electrodes 46 may be ring electrodes. The configuration, type and numberof electrodes 46 illustrated in FIG. 3 are merely exemplary. Forexample, leads 24 and 26 may each include eight or any other number ofelectrodes 46, and the electrodes 46 need not be arranged linearly oneach of leads 24 and 26 or be ring electrodes.

Electrodes 46 are electrically coupled to a multiplexer 58. Multiplexer58 is able to selectively couple each of the electrodes to circuitswithin IMD 18 under the control of a processor 52. For example, throughmultiplexer 58, processor 52 may selectively couple electrodes 46 to atherapy module 56 or EEG signal module 60.

Therapy module 56 may, for example, include an output pulse generatorcoupled to a power source 66, which may include a primary orrechargeable battery. Therapy module 56 may deliver electrical pulses topatient 12 via at least some of electrodes 46 under the control of aprocessor 52, which controls therapy delivery module 56 to deliverneurostimulation therapy according to a current therapy parameter set.However, the invention is not limited to implantable neurostimulatorembodiments or even to IMDs that deliver electrical stimulation. Forexample, in some embodiments a therapy delivery module 56 of an IMD mayinclude a pump, circuitry to control the pump, and a reservoir to storea therapeutic agent for delivery via the pump. Further, in someembodiments, therapy delivery module 56 may deliver a thermal therapy,e.g., may include or be coupled to thermal transducer, such as Peltiereffect device. The therapy parameter sets used by processor 52 tocontrol delivery therapy by therapy module 56 may be received via atelemetry module 64 and/or stored in memory 54.

Processor 52 may include a microprocessor, a controller, a digitalsignal processor (DSP), an application specific integrated circuit(ASIC), a field-programmable gate array (FPGA), discrete logiccircuitry, or the like. Memory 54 may include any volatile,non-volatile, magnetic, optical, or electrical media, such as a randomaccess memory (RAM), read-only memory (ROM), non-volatile RAM (NVRAM),electrically-erasable programmable ROM (EEPROM), flash memory, and thelike. In some embodiments, memory 54 stores program instructions that,when executed by processor 52, cause IMD 18 and processor 52 to performthe functions attributed to them herein.

EEG signal module 60 receives signals from a selected set of theelectrodes 46 via multiplexer 58 as controlled by processor 52. EEGsignal module 60 may analyze the EEG signal for certain featuresindicative of sleep or different sleep states, and provide indicationsof relating to sleep or sleep states to processor 52. IMD 18 may includecircuitry (not shown) that conditions the EEG signal such that it may beanalyzed by processor 52. For example, IMD 18 may include one or moreanalog to digital converters to convert analog signals generated bysensor 50 into digital signals usable processor 52, as well as suitablefilter and amplifier circuitry.

In some embodiments, processor 52 will only request EEG signal module 60to operate when one or more other physiological parameters indicate thatpatient 12 is already asleep. However, processor 52 may also direct EEGsignal module to analyze the EEG signal to determine whether patient 12is sleeping, and such analysis may be considered alone or in combinationwith other physiological parameters to determine whether patient 12 isasleep. In some embodiments, the functionality of EEG signal module 60may be provided by processor 52, which, as described above, may includeone or more microprocessors, ASICs, or the like.

Sensors 50 generate a signal as a function of one or more physiologicalparameters of patient 12. IMD 18 may include circuitry (not shown) thatconditions the signals generated by sensors 50 such that they may beanalyzed by processor 52, e.g., analog-to-digital converters,amplifiers, and/or filters. Although shown as including two sensors 50,system 10 may include any number of sensors.

Further, as illustrated by FIG. 3, sensors 50 may be included as part ofIMD 18, or coupled to IMD 18 via lead 48, as illustrated in FIG. 3. Lead48 may be bundled with leads 24 and 26 in some embodiments. Further, issome embodiments, a sensor 50 may be coupled to IMD 18 via therapy leads24 and 26. In some embodiments, a sensor 50 located outside of IMD 18may be in wireless communication with IMD 18. Wireless communicationbetween sensor 50 and IMD 18 may, as examples, include RF communicationor communication via electrical signals conducted through the tissueand/or fluid of patient 12.

As discussed above, exemplary physiological parameters of patient 12that may be monitored by IMD 18 to determine values of one or more sleepquality metrics include activity level, posture, heart rate, ECGmorphology, respiration rate, respiratory volume, blood pressure, bloodoxygen saturation, partial pressure of oxygen within blood, partialpressure of oxygen within cerebrospinal fluid, muscular activity andtone, core temperature, subcutaneous temperature, arterial blood flow,the level of melatonin within a bodily fluid of patient 12, electricalactivity of the brain of the patient, e.g., EEG or EEG morphology, andeye motion. Further, as discussed above, in some external medical deviceembodiments of the invention, galvanic skin response may additionally oralternatively be monitored. Sensors 50 may include any type of sensorknown in the art capable of generating a signal as a function of one ormore of these parameters. In some embodiments, processor 52 may receivean EEG signal from sensors 50 instead of electrodes 46.

In some embodiments, in order to determine one or more sleep qualitymetric values, processor 52 determines when patient 12 is attempting tofall asleep. For example, processor 52 may identify the time thatpatient begins attempting to fall asleep based on an indication receivedfrom patient 12, e.g., via programmer 28 and a telemetry circuit 64. Inother embodiments, processor 52 identifies the time that patient 12begins attempting to fall asleep based on the activity level of patient12.

In such embodiments, IMD 18 may include one or more sensors 50 thatgenerate a signal as a function of patient activity. For example,sensors 50 may include one or more accelerometers, gyros, mercuryswitches, or bonded piezoelectric crystals that generates a signal as afunction of patient activity, e.g., body motion, footfalls or otherimpact events, and the like. Additionally or alternatively, sensor 50may include one or more electrodes that generate an electromyogram (EMG)signal as a function of muscle electrical activity, which may indicatethe activity level of a patient. The electrodes may be, for example,located in the legs, abdomen, chest, back or buttocks of patient 12 todetect muscle activity associated with walking, running, or the like.The electrodes may be coupled to IMD 18 wirelessly or by lead 48 or, ifIMD 18 is implanted in these locations, integrated with a housing of IMD18.

However, bonded piezoelectric crystals located in these areas generatesignals as a function of muscle contraction in addition to body motion,footfalls or other impact events. Consequently, use of bondedpiezoelectric crystals to detect activity of patient 12 may be preferredin some embodiments in which it is desired to detect muscle activity inaddition to body motion, footfalls, or other impact events. Bondedpiezoelectric crystals may be coupled to IMD 18 wirelessly or via lead48, or piezoelectric crystals may be bonded to the can of IMD 18 whenthe IMD is implanted in these areas, e.g., in the back, chest, buttocksor abdomen of patient 12.

Processor 52 may identify a time when the activity level of patient 12falls below a threshold activity level value stored in memory 54, andmay determine whether the activity level remains substantially below thethreshold activity level value for a threshold amount of time stored inmemory 54. In other words, patient 12 remaining inactive for asufficient period of time may indicate that patient 12 is attempting tofall asleep. If processor 52 determines that the threshold amount oftime is exceeded, processor 52 may identify the time at which theactivity level fell below the threshold activity level value as the timethat patient 12 began attempting to fall asleep.

In some embodiments, processor 52 determines whether patient 12 isattempting to fall asleep based on whether patient 12 is or is notrecumbent, e.g., lying down. In such embodiments, sensors 50 may includea plurality of accelerometers, gyros, or magnetometers orientedorthogonally that generate signals which indicate the posture of patient12. In addition to being oriented orthogonally with respect to eachother, sensors 50 used to detect the posture of patient 12 may begenerally aligned with an axis of the body of patient 12. In exemplaryembodiments, IMD 18 includes three orthogonally oriented posture sensors50.

When sensors 50 include accelerometers, for example, that are aligned inthis manner, processor 52 may monitor the magnitude and polarity of DCcomponents of the signals generated by the accelerometers to determinethe orientation of patient 12 relative to the Earth's gravity, e.g., theposture of patient 12. In particular, the processor 52 may compare theDC components of the signals to respective threshold values stored inmemory 54 to determine whether patient 12 is or is not recumbent.Further information regarding use of orthogonally aligned accelerometersto determine patient posture may be found in a commonly assigned U.S.Pat. No. 5,593,431, which issued to Todd J. Sheldon.

Sensors 50 that may generate a signal that indicates the posture ofpatient 12 may include electrodes that generate an electromyogram (EMG)signal, or bonded piezoelectric crystals that generate a signal as afunction of contraction of muscles. This type of sensor 50 may beimplanted in the legs, buttocks, chest, abdomen, or back of patient 12,as described herein. The signals generated by such sensors whenimplanted in these locations may vary based on the posture of patient12, e.g., may vary based on whether the patient is standing, sitting, orlaying down.

Further, changes of the posture of patient 12 may cause pressure changeswithin the cerebrospinal fluid (CSF) of the patient. Consequently,sensors 50 may include pressure sensors coupled to one or moreintrathecal, intracerebroventricular, or subarachnoid catheters, orpressure sensors in such locations coupled to IMD 18 wirelessly or vialead 48. CSF pressure changes associated with posture changes may beparticularly evident within the brain of the patient, e.g., may beparticularly apparent in an intracranial pressure (ICP) waveform. WhileCSF pressure may be particularly practical as a means of detectingposture in cranially implanted IMD embodiments, or other embodimentswith leads located in the brain, use of CSF pressure to detect postureis not limited to such embodiments.

In some embodiments, processor 52 considers both the posture and theactivity level of patient 12 when determining whether patient 12 isattempting to fall asleep. For example, processor 52 may determinewhether patient 12 is attempting to fall asleep based on a sufficientlylong period of sub-threshold activity, as described above, and mayidentify the time that patient 12 began attempting to fall asleep as thetime when patient 12 became recumbent.

In other embodiments, processor 52 determines when patient 12 isattempting to fall asleep based on the level of melatonin in a bodilyfluid. In such embodiments, sensors 50 may include a chemical sensorthat is sensitive to the level of melatonin or a metabolite of melatoninin the bodily fluid, and estimate the time that patient 12 will attemptto fall asleep based on the detection. For example, processor 52 maycompare the melatonin level or rate of change in the melatonin level toa threshold level stored in memory 54, and identify the time thatthreshold value is exceeded. Processor 52 may identify the time thatpatient 12 is attempting to fall asleep as the time that the thresholdis exceeded, or some amount of time after the threshold is exceeded. Anyof a variety of combinations or variations of the above-describedtechniques may be used to determine when patient 12 is attempting tofall asleep, and a specific one or more techniques may be selected basedon the sleeping and activity habits of a particular patient.

Processor 52 may also determine when patient 12 is asleep, e.g.,identify the times that patient 12 falls asleep and wakes up, in orderto determine one or more sleep quality metric values. The detectedvalues of physiological parameters of patient 12, such as activitylevel, heart rate, ECG morphological features, respiration rate,respiratory volume, blood pressure, blood oxygen saturation, partialpressure of oxygen within blood, partial pressure of oxygen withincerebrospinal fluid, muscular activity and tone, core temperature,subcutaneous temperature, arterial blood flow, EEG activity and/ormorphology, eye motion and galvanic skin response may discernibly changewhen patient 12 falls asleep or wakes up. Some of these physiologicalparameters may be at low values when patient 12 is asleep. Further, thevariability of at least some of these parameters, such as heart rate andrespiration rate, may be at a low value when the patient is asleep.

Consequently, in order to detect when patient 12 falls asleep and wakesup, processor 52 may monitor one or more of these physiologicalparameters, or the variability of these physiological parameters, anddetect the discernable changes in their values associated with atransition between a sleeping state and an awake state. In someembodiments, processor 52 may determine a mean or median value for aparameter based on values of a signal over time, and determine whetherpatient 12 is asleep or awake based on the mean or median value.Processor 52 may compare one or more parameter or parameter variabilityvalues to thresholds stored in memory 54 to detect when patient 12 fallsasleep or wakes. The thresholds may be absolute values of aphysiological parameter, or time rate of change values for thephysiological parameter, e.g., to detect sudden changes in the value ofa parameter or parameter variability. In some embodiments, a thresholdused by processor 52 to determine whether patient 12 is asleep mayinclude a time component. For example, a threshold may require that aphysiological parameter be above or below a threshold value for a periodof time before processor 52 determines that patient 12 is awake orasleep.

In some embodiments, in order to determine whether patient 12 is asleep,processor 52 monitors a plurality of physiological parameters, anddetermines a value of a metric that indicates the probability thatpatient 12 is asleep for each of the parameters based on a value of theparameter. In particular, the processor 52 may apply a function orlook-up table to the current, mean or median value, and/or thevariability of each of a plurality of physiological parameters todetermine a sleep probability metric for each of the plurality ofphysiological parameters. A sleep probability metric value may be anumeric value, and in some embodiments may be a probability value, e.g.,a number within the range from 0 to 1, or a percentage value.

Processor 52 may average or otherwise combine the plurality of sleepprobability metric values to provide an overall sleep probability metricvalue. In some embodiments, processor 52 may apply a weighting factor toone or more of the sleep probability metric values prior to combination.Processor 52 may compare the overall sleep probability metric value toone or more threshold values stored in memory 54 to determine whenpatient 12 falls asleep or awakes. Use of sleep probability metricvalues to determine when a patient is asleep based on a plurality ofmonitored physiological parameters is described in greater detail in acommonly-assigned and copending U.S. patent application Ser. No.11/081,786 by Ken Heruth and Keith Miesel, entitled “DETECTING SLEEP,”which was assigned and filed on Mar. 16, 2005, and is incorporatedherein by reference in its entirety.

To enable processor 52 to determine when patient 12 is asleep or awake,sensors 50 may include, for example, activity sensors as describedabove. In some embodiments, the activity sensors may include electrodesor bonded piezoelectric crystals, which may be implanted in the back,chest, buttocks, or abdomen of patient 12 as described above. In suchembodiments, processor 52 may detect the electrical activation andcontractions of muscles associated with gross motor activity of thepatient, e.g., walking, running or the like via the signals generated bysuch sensors. Processor 52 may also detect spasmodic, irregular,movement disorder or pain related muscle activation via the signalsgenerated by such sensors. Such muscle activation may indicate thatpatient 12 is not sleeping, e.g., unable to sleep, or if patient 12 issleeping, may indicate a lower level of sleep quality.

As another example, sensors 50 may include electrodes located on leadsor integrated as part of the housing of IMD 18 that generate anelectrogram signal as a function of electrical activity of the heart ofpatient 12, and processor 52 may monitor the heart rate of patient 12based on the electrogram signal. In other embodiments, a sensor mayinclude an acoustic sensor within IMD 18, a pressure or flow sensorwithin the bloodstream or cerebrospinal fluid of patient 12, or atemperature sensor located within the bloodstream of patient 12. Thesignals generated by such sensors may vary as a function of contractionof the heart of patient 12, and can be used by IMD 18 to monitor theheart rate of patient 12.

In some embodiments, processor 52 may detect, and measure values for oneor more ECG morphological features within an electrogram generated byelectrodes as described above. ECG morphological features may vary in amanner that indicates whether patient 12 is asleep or awake. Forexample, the amplitude of the ST segment of the ECG may decrease whenpatient 12 is asleep. Further, the amplitude of QRS complex or T-wavemay decrease, and the widths of the QRS complex and T-wave may increasewhen patient 12 is asleep. The QT interval and the latency of an evokedresponse may increase when patient 12 is asleep, and the amplitude ofthe evoked response may decrease when patient 12 is asleep.

In some embodiments, sensor 50 may include an electrode pair includingat least one electrode within or proximate to the thorax, e.g., anelectrode integrated with the housing of IMD 18 or on a lead implantedat such a location, that generates a signal as a function of thethoracic impedance of patient 12, as described above. The thoracicimpedance signal varies as a function of respiration by patient 12. Inother embodiments, sensors 50 may include a strain gage, bondedpiezoelectric element, or pressure sensor within the blood orcerebrospinal fluid that generates a signal that varies based on patientrespiration. An electrogram generated by electrodes as discussed abovemay also be modulated by patient respiration, and may be used as anindirect representation of respiration rate.

Sensors 50 may include electrodes that generate an electromyogram (EMG)signal as a function of muscle electrical activity, as described above,or may include any of a variety of known temperature sensors to generatea signal as a function of a core or subcutaneous temperature of patient12. Such electrodes and temperature sensors may be incorporated withinthe housing of IMD 18, or coupled to IMD 18 wirelessly via leads.Sensors 50 may also include a pressure sensor within, or in contactwith, a blood vessel. The pressure sensor may generate a signal as afunction of the blood pressure of patient 12, and may, for example,comprise a Chronicle Hemodynamic Monitor™ commercially available fromMedtronic, Inc. of Minneapolis, Minn. Further, certain muscles ofpatient 12, such as the muscles of the patient's neck, may discerniblyrelax when patient 12 is asleep or within certain sleep states.Consequently, sensors 50 may include strain gauges or EMG electrodesimplanted in such locations that generate a signal as a function ofmuscle tone.

Sensors 50 may also include optical pulse oximetry sensors or Clarkdissolved oxygen sensors located within, as part of a housing of, oroutside of IMD 18, which generate signals as a function of blood oxygensaturation and blood oxygen partial pressure respectively. In someembodiments, system 10 may include a catheter with a distal portionlocated within the cerebrospinal fluid of patient 12, and the distal endmay include a Clark dissolved oxygen sensor to generate a signal as afunction of the partial pressure of oxygen within the cerebrospinalfluid. Embodiments in which an IMD comprises an implantable pump, forexample, may include a catheter with a distal portion located in thecerebrospinal fluid;

In some embodiments, sensors 50 may include one or more intraluminal,extraluminal, or external flow sensors positioned to generate a signalas a function of arterial blood flow. A flow sensor may be, for example,an electromagnetic, thermal convection, ultrasonic-Doppler, orlaser-Doppler flow sensor. Further, in some external medical deviceembodiments of the invention, sensors 50 may include one or moreelectrodes positioned on the skin of patient 12 to generate a signal asa function of galvanic skin response.

Also, the motion of the eyes of patient 12 may vary depending on whetherthe patient is sleeping and which sleep state the patient is in.Consequently, sensors 50 may include electrodes placed proximate to theeyes of patient 12 to detect electrical activity associated with motionof the eyes, e.g., to generate an electro-oculography (EOG) signal. Suchelectrodes may be coupled to IMD 18 via one or more lead 48, or may beincluded within modules that include circuitry to wirelessly transmitdetected signals to IMD 18. Wirelessly coupled modules incorporatingelectrodes to detect eye motion may be worn externally by patient 12,e.g., attached to the skin of patient 12 proximate to the eyes by anadhesive when the patient is attempting to sleep.

Further, processor 52 may determine whether patient 12 is asleep basedon indications received from EEG signal module 60 based on its analysisof the EEG received via electrodes 46. EEG signal module 60 may processthe EEG signals to detect when patient 12 is asleep using any of avariety of techniques, such as techniques that identify whether apatient is asleep based on the amplitude and/or frequency of the EEGsignals.

Processor 52 may also detect arousals and/or apneas that occur whenpatient 12 is asleep based on one or more of the above-identifiedphysiological parameters. For example, processor 52 may detect anarousal based on an increase or sudden increase in one or more of heartrate, heart rate variability, respiration rate, respiration ratevariability, blood pressure, or muscular activity as the occurrence ofan arousal. Processor 52 may detect an apnea based on a disturbance inthe respiration rate of patient 12, e.g., a period with no respiration.

Processor 52 may also detect arousals or apneas based on sudden changesin one or more of the ECG morphological features identified above. Forexample, a sudden elevation of the ST segment within the ECG mayindicate an arousal or an apnea. Further, sudden changes in theamplitude or frequency of an EEG signal, EOG signal, or muscle tonesignal may indicate an apnea or arousal. Memory 54 may store thresholdsused by processor 52 to detect arousals and apneas. Processor 52 maydetermine, as a sleep quality metric value, the number of apnea eventsand/or arousals during a night.

Further, in some embodiments, processor 52 may determine which sleepstate patient 12 is in during sleep, e.g., REM, S1, S2, S3, or S4, basedon one or more of the monitored physiological parameters, and/orindications received from EEG signal module 60. In some embodiments,memory 54 may store one or more thresholds for each of sleep states, andprocessor 52 may compare physiological parameter or sleep probabilitymetric values to the thresholds to determine which sleep state patient12 is currently in.

Further, in some embodiments, EEG signal module 60 may use any of avariety of techniques for determining which sleep state patient is inbased on an EEG signal, which processor 52 may receive via electrodes 46as described above, such as techniques that identify sleep state basedon the amplitude and/or frequency of the EEG signals. In someembodiments, processor 52 may also determine which sleep state patientis in based on an EOG signal, which processor 52 may receive viaelectrodes as described above, either alone or in combination withindications received from EEG signal module 60.

FIG. 4 is a logical diagram of an example circuit that detects the sleeptype of a patient based on the electroencephalogram (EEG) signal. Asshown in FIG. 4, module 68 may be integrated into EEG signal module 60of FIG. 3. An EEG signal detected by electrodes 46 is transmitted intomodule 68 and provided to three channels, each of which includes arespective one of amplifiers 70, 86 and 102, and bandpass filters 72, 88and 104. In other embodiments, a common amplifier amplifies the EEGsignal prior to filters 72, 88 and 104.

Bandpass filter 72 allows frequencies between approximately 4 Hz andapproximately 8 Hz, and signals within the frequency range may beprevalent in the EEG during S1 and S2 sleep states. Bandpass filter 88allows frequencies between approximately 1 Hz and approximately 3 Hz,which may be prevalent in the EEG during the S3 and S4 sleep states.Bandpass filter 104 allows frequencies between approximately 10 Hz andapproximately 50 Hz, which may be prevalent in the EEG during REM sleep.Each resulting signal may then processed to identify in which sleepstate patient 12 is.

After bandpass filtering of the original EEG signal, the filteredsignals are similarly processed in parallel before being delivered tosleep logic module 118. For ease of discussion, only one of the threechannels will be discussed herein, but each of the filtered signalswould be processed similarly.

Once the EEG signal is filtered by bandpass filter 72, the signal isrectified by full-wave rectifier 74. Modules 76 and 78 respectivelydetermine the foreground average and background average so that thecurrent energy level can be compared to a background level at comparator82. The signal from background average is increased by gain 80 beforesent to comparator 82, because comparator 82 operates in the range ofmillivolts or volts while the EEG signal amplitude is originally on theorder of microvolts. The signal from comparator 82 is indicative ofsleep stages S1 and S2. If duration logic 84 determines that the signalis greater than a predetermined level for a predetermined amount oftime, the signal is sent to sleep logic module 118 indicating thatpatient 12 may be within the S1 or S2 sleep states. In some embodiments,as least duration logic 84, 100, 116 and sleep logic 118 may be embodiedin processor 52 (FIG. 3).

Module 68 may detect all sleep types for patient 12. Further, thebeginning of sleep may be detected by module 68 based on the sleep stateof patient 12. Some of the components of module 68 may vary from theexample of FIG. 4. For example, gains 80, 96 and 112 may be providedfrom the same power source. Module 68 may be embodied as analogcircuitry, digital circuitry, or a combination thereof.

In other embodiments, FIG. 4 may not need to reference the backgroundaverage to determine the current state of sleep of patient 12. Instead,the power of the signals from bandpass filters 72, 88 and 104 arecompared to each other, and sleep logic module 118 determines which thesleep state of patient 12 based upon the frequency band that has thehighest power. In this case, the signals from full-wave rectifiers 74,90 and 106 are sent directly to a device that calculates the signalpower, such as a spectral power distribution module (PSD), and then tosleep logic module 118 which determines the frequency band of thegreatest power, e.g., the sleep state of patient 12. In some cases, thesignal from full-wave rectifiers 74, 90 and 106 may be normalized by again component to correctly weight each frequency band.

FIG. 5 is a conceptual diagram illustrating another example system 120that includes an implantable medical device (IMD) 122 that collectsinformation relating to the quality of sleep experienced by a patient 12according to the invention. System 120 may be substantially similar infunction to systems 10 and 30 of FIGS. 1 and 3, respectively. IMD 122may be similar to IMDs 18 and 32, and may determine sleep quality metricvalues based on physiological parameter signals generated by sensors asdiscussed above with respect to IMD 18. However, unlike IMDs 18 and 32,IMD 122 delivers spinal cord stimulation (SCS) via leads 124A and 124B(collectively “leads 124”) implanted proximate to the spinal cord 126 ofpatient 12 in order to, for example, reduce pain experienced by patient12.

Like the IMDs discussed above, IMD 122 delivers therapy according to aset of therapy parameters, i.e., a set of values for a number ofparameters that define the therapy delivered according to that therapyparameter set, which may include voltage or current pulse amplitudes,pulse widths, pulse rates, and information identifying which electrodes(not shown) on leads 124 have been selected for delivery of pulses, andthe polarities of the selected electrodes.

FIG. 5 also illustrates system 120 as including a clinician programmer128 and a patient programmer 134. Clinician programmer 128 and patientprogrammer 134 may be similar to programmer 28 of FIGS. 1 and 2. Aclinician (not shown) may use clinician programmer 128 to programtherapy for patient 12, e.g., specify a number of therapy parameter setsand provide the parameter sets to IMD 122. The clinician may also useclinician programmer 128 to retrieve information collected by IMD 122.The clinician may use clinician programmer 128 to communicate with IMD122 both during initial programming of IMD 122, and for collection ofinformation and further programming during follow-up visits.

Clinician programmer 128 may, as shown in FIG. 5, be a handheldcomputing device. Clinician programmer 128 includes a display 130, suchas a LCD or LED display, to display information to a user. Clinicianprogrammer 128 may also include a keypad 132, which may be used by auser to interact with clinician programmer 128. In some embodiments,display 130 may be a touch screen display, and a user may interact withclinician programmer 128 via display 130. A user may also interact withclinician programmer 128 using peripheral pointing devices, such as astylus or mouse. Keypad 132 may take the form of an alphanumeric keypador a reduced set of keys associated with particular functions.

Patient programmer 134 also may, as shown in FIG. 5, be a handheldcomputing device. Patient 12 may use patient programmer 134 to controlthe delivery of therapy by IMD 122. For example, using patientprogrammer 134, patient 12 may select a current therapy parameter setfrom among the therapy parameter sets preprogrammed by the clinician, ormay adjust one or more parameters of a preprogrammed therapy parameterset to arrive at the current therapy parameter set.

Patient programmer 134 may include a display 136 and a keypad 138, toallow patient 12 to interact with patient programmer 134. In someembodiments, display 136 may be a touch screen display, and patient 12may interact with patient programmer 134 via display 136. Patient 12 mayalso interact with patient programmer 134 using peripheral pointingdevices, such as a stylus, mouse, or the like.

However, clinician and patient programmers 128, 134 are not limited tothe hand-held computer embodiments illustrated in FIG. 5. Programmers128, 134 according to the invention may be any sort of computing device.For example, a programmer 128, 134 according to the invention may be atablet-based computing device, a desktop computing device, or aworkstation.

IMD 122, clinician programmer 128 and patient programmer 134 may, asshown in FIG. 5, communicate via wireless communication. Clinicianprogrammer 128 and patient programmer 134 may, for example, communicatevia wireless communication with IMD 122 using radio frequency (RF)telemetry techniques known in the art. Clinician programmer 128 andpatient programmer 134 may communicate with each other using any of avariety of local wireless communication techniques, such as RFcommunication according to the 802.11 or Bluetooth specification sets,infrared communication according to the IRDA specification set, or otherstandard or proprietary telemetry protocols.

Clinician programmer 128 and patient programmer 134 need not communicatewirelessly, however. For example, programmers 128 and 134 maycommunicate via a wired connection, such as via a serial communicationcable, or via exchange of removable media, such as magnetic or opticaldisks, or memory cards or sticks. Further, clinician programmer 128 maycommunicate with one or both of IMD 122 and patient programmer 134 viaremote telemetry techniques known in the art, communicating via a localarea network (LAN), wide area network (WAN), public switched telephonenetwork (PSTN), or cellular telephone network, for example.

One or both of programmers 128, 134 may receive sleep quality metricvalues from IMD 122, and may provide sleep quality information to a userbased on the sleep quality metric values. For example, patientprogrammer 134 may provide a message to patient 12, e.g., via display136, related to sleep quality based on received sleep quality metricvalues. Patient programmer 134 may, for example, suggest that patient 12visit a clinician for prescription of sleep medication or for anadjustment to the therapy delivered by IMD 122. As other examples,patient programmer 134 may suggest that patient 12 increase theintensity of therapy delivered by IMD 122 during nighttime hoursrelative to previous nights, or select a different therapy parameter setfor use by IMD 122 than the patient had selected during previous nights.Further, patient programmer 134 may report the quality of the patient'ssleep to patient 12 to, for example, provide patient 12 with anobjective indication of whether his or her sleep quality is good,adequate, or poor.

FIG. 6 is a block diagram further illustrating system 120. System 120may function similar to systems 10 and 30 as described in FIG. 3. Inparticular, FIG. 6 illustrates an example configuration of IMD 122 andleads 124A and 124B. FIG. 6 also illustrates sensors 142A and 142B(collectively “sensors 142”) that generate signals as a function of oneor more physiological parameters of patient 12, which may besubstantially similar to sensors 50 discussed above with respect to FIG.3.

In the illustrated example, IMD 122 is coupled to leads 124A and 124B,which respectively include electrodes 140A-D and electrodes 140E-H(collectively “electrodes 140”). Electrodes 140 may be ring electrodes.The configuration, type and number of electrodes 140 illustrated in FIG.6 are merely exemplary.

Electrodes 140 are electrically coupled to a therapy delivery module 144via leads 124, which may be substantially similar to therapy module 56discussed above with respect to FIG. 3. Further, IMD 122 includes aprocessor 146, memory 148 and telemetry module 150 substantially similarto and providing substantially the same functionality as the processor52, memory 54 and telemetry module 64 of FIG. 3.

Because leads 124 are not implanted within or proximate to brain, IMD122 does not include an EEG signal module 60 in the illustratedembodiment. In other embodiments, IMD 122 may nonetheless monitor theEEG as described above. In such embodiments, IMD 122 may include an EEGsignal module 60, or processor 146 may provide similar functionality. Insuch embodiments, sensors 142 may include one or more electrodespositioned within or proximate to the brain of patient 12, which detectelectrical activity of the brain. Processor 146 may be wirelesslycoupled to electrodes that detect brain electrical activity.

For example, one or more modules may be implanted beneath the scalp ofthe patient, each module including a housing, one or more electrodes,and circuitry to wirelessly transmit the signals detected by the one ormore electrodes to IMD 122. In other embodiments, the electrodes may beapplied to the patient's scalp, and electrically coupled to a modulethat includes circuitry for wirelessly transmitting the signals detectedby the electrodes to IMD 122. The electrodes may be glued to thepatient's scalp, or a head band, hair net, cap, or the like mayincorporate the electrodes and the module, and may be worn by patient 12to apply the electrodes to the patient's scalp when, for example, thepatient is attempting to sleep. The signals detected by the electrodesand transmitted to IMD 122 may be electroencephalogram (EEG) signals,and processor 146 may process the EEG signals to detect when patient 12is asleep using any of a variety of known techniques, such as techniquesthat identify whether a patient is asleep based on the amplitude and/orfrequency of the EEG signals. FIGS. 3 and 4 describe methods foranalyzing and processing the EEG signal.

FIG. 7 further illustrates memory 148 of IMD 122, to which memories 54of IMDs 18 and 32 may be substantially similar. As illustrated in FIG.7, memory 148 stores information describing a plurality of therapyparameter sets 152. Therapy parameter sets 152 may include parametersets specified by a clinician using clinician programmer 128. Therapyparameter sets 152 may also include parameter sets that are the resultof patient 12 changing one or more parameters of one of thepreprogrammed therapy parameter sets via patient programmer 134.

Memory 148 may also include parameter information 154 recorded byprocessor 146, e.g., physiological parameter values, or mean or medianphysiological parameter values. Memory 148 stores threshold values 156used by processor 146 in the collection of sleep quality metric values,as discussed above. In some embodiments, memory 148 also stores one ormore functions or look-up tables (not shown) used by processor 146 todetermine sleep probability metric values, or to determine an overallsleep quality metric value.

Further, processor 146 stores determined values 158 for one or moresleep quality metrics within memory 148. Processor 146 may collect sleepquality metric values 158 each time patient 12 sleeps, or only duringselected times that patient 12 is asleep. Processor 146 may store eachsleep quality metric value determined within memory 148 as a sleepquality metric value 158, or may store mean or median sleep qualitymetric values over periods of time such as weeks or months as sleepquality metric values 158. Further, processor 146 may apply a functionor look-up table to a plurality of sleep quality metric values todetermine overall sleep quality metric value, and may store the overallsleep quality metric values within memory 148. The application of afunction or look-up table by processor 146 for this purpose may involvethe use or weighting factors for one or more of the individual sleepquality metric values.

In some embodiments, processor 146 identifies which of therapy parametersets 152 is currently selected for use in delivering therapy to patient12 when a value of one or more sleep quality metrics is collected, andmay associate that value with the current therapy parameter set. Forexample, for each of the plurality of therapy parameter sets 152,processor 146 may store a representative value of each of one or moresleep quality metrics within memory 148 as a sleep quality metric value158 with an indication of which of the therapy parameter sets thatrepresentative value is associated with. A representative value of sleepquality metric for a therapy parameter set may be the mean or median ofcollected sleep quality metric values that have been associated withthat therapy parameter set.

As shown in FIG. 6, IMD 122 also includes a telemetry circuit 150 thatallows processor 146 to communicate with clinician programmer 128 andpatient programmer 134. Processor 146 may receive informationidentifying therapy parameter sets 152 preprogrammed by the clinicianand threshold values 156 from clinician programmer 128 via telemetrycircuit 150 for storage in memory 148. Processor 146 may receive anindication of the therapy parameter set 152 selected by patient 12 fordelivery of therapy, or adjustments to one or more of therapy parametersets 152 made by patient 12, from patient programmer 134 via telemetrycircuit 150. Programmers 128, 134 may receive sleep quality metricvalues 158 from processor 146 via telemetry circuit 150.

FIG. 8 is a flow diagram illustrating an example method for collectingsleep quality information that may be employed by any of IMDs 18, 32 and122, but IMD 18 will be used herein as an example. IMD 18 monitors theposture, activity level, and/or melatonin level of patient 12, ormonitors for an indication from patient 12, e.g., via patient programmer28 (160), and determines whether patient 12 is attempting to fall asleepbased on the posture, activity level, melatonin level, and/or a patientindication, as described above (162). If IMD 18 determines that patient12 is attempting to fall asleep, IMD 18 identifies the time that patient12 began attempting to fall asleep using any of the techniques describedabove (164), and monitors one or more of the various physiologicalparameters of patient 12 discussed above to determine whether patient 12is asleep (166, 168).

In some embodiments, IMD 18 compares parameter values or parametervariability values to one or more threshold values 156 to determinewhether patient 12 is asleep. In other embodiments, IMD 18 applies oneor more functions or look-up tables to determine one or more sleepprobability metric values based on the physiological parameter values,and compares the sleep probability metric values to one or morethreshold values 156 to determine whether patient 12 is asleep. Whilemonitoring physiological parameters (166) to determine whether patient12 is asleep (168), IMD 18 may continue to monitor the posture and/oractivity level of patient 12 (160) to confirm that patient 12 is stillattempting to fall asleep (162).

When IMD 18 determines that patient 12 is asleep, e.g., by analysis ofthe various parameters contemplated herein, IMD 18 will identify thetime that patient 12 fell asleep (170). While patient 12 is sleeping,IMD 18 will continue to monitor physiological parameters of patient 12(172). As discussed above, IMD 18 may identify the occurrence ofarousals and/or apneas based on the monitored physiological parameters(174). Further, IMD 18 may identify the time that transitions betweensleep states, e.g., REM, S1, S2, S3, and S4, occur based on themonitored physiological parameters, e.g., via the EEG using the circuit68 discussed above with reference to FIG. 4 (174).

Additionally, while patient 12 is sleeping, IMD 18 monitorsphysiological parameters of patient 12 (172) to determine whetherpatient 12 has woken up (176). When IMD 18 determines that patient 12 isawake, IMD 18 identifies the time that patient 12 awoke (178), anddetermines sleep quality metric values based on the informationcollected while patient 12 was asleep (180).

For example, one sleep quality metric value IMD 18 may calculate issleep efficiency, which IMD 18 may calculate as a percentage of timeduring which patient 12 is attempting to sleep that patient 12 isactually asleep. IMD 18 may determine a first amount of time between thetime IMD 18 identified that patient 12 fell asleep and the time IMD 18identified that patient 12 awoke. IMD 18 may also determine a secondamount of time between the time IMD 18 identified that patient 12 beganattempting to fall asleep and the time IMD 18 identified that patient 12awoke. To calculate the sleep efficiency, IMD 18 may divide the firsttime by the second time.

Another sleep quality metric value that IMD 18 may calculate is sleeplatency, which IMD 18 may calculate as the amount of time between thetime IMD 18 identified that patient 12 was attempting to fall asleep andthe time IMD 18 identified that patient 12 fell asleep. Other sleepquality metrics with values determined by IMD 18 based on theinformation collected by IMD 18 in the illustrated example include:total time sleeping per day, at night, and during daytime hours; numberof apnea and arousal events per occurrence of sleep; and amount of timespent in the various sleep states, e.g., one or both of the S3 and S4sleep states. IMD 18 may store the determined values as sleep qualitymetric values 158 within memory 54.

IMD 18 may perform the example method illustrated in FIG. 8continuously, e.g., may monitor to identify when patient 12 isattempting to sleep and asleep any time of day, each day. In otherembodiments, IMD 18 may only perform the method during evening hoursand/or once every N days to conserve battery and memory resources.Further, in some embodiments, IMD 18 may only perform the method inresponse to receiving a command from patient 12 or a clinician via one aprogrammer 28. For example, patient 12 may direct IMD 18 to collectsleep quality information at times when the patient believes that his orher sleep quality is low or therapy is ineffective.

FIG. 9 is a flow diagram illustrating another example method forcollecting sleep quality information that may be employed by IMD 18, 32or 122. IMD 18 will be used as an example, and specifically, IMD 18 usesthe EEG signal to determine sleep state. IMD 18 monitors the posture andactivity level of patient 12, or monitors for an indication from patient12, e.g., via programmer 28 (182), and determines whether patient 12 isattempting to fall asleep based on the posture, activity level,melatonin level, and/or a patient indication, as described above (184).If IMD 18 determines that patient 12 is attempting to fall asleep, IMD18 identifies the time that patient 12 began attempting to fall asleepusing any of the techniques described above (186), and monitors one ormore of the various physiological parameters of patient 12 discussedabove to determine whether patient 12 is asleep (188, 190).

In some embodiments, IMD 18 compares parameter values or parametervariability values to one or more threshold values 156 to determinewhether patient 12 is asleep. In other embodiments, IMD 18 applies oneor more functions or look-up tables to determine one or more sleepprobability metric values based on the physiological parameter values,and compares the sleep probability metric values to one or morethreshold values 156 to determine whether patient 12 is asleep.

In some embodiments, IMD 18 analyzes the amplitude and/or frequency ofthe EEG, alone or in combination with other physiological parametersignals, to determine whether patient is asleep. For example, IMD 18 mayanalyze one or more of posture, activity, ECG, or other physiologicalsignals discussed above in combination with the EEG, e.g., using sleepprobability values for each signal, to determine whether the signalsconsidered in combination indicate that patient 12 is asleep. In somecase, circuit 68 may be used to analyze the EEG for this purpose by, forexample, indicating that patient 12 is asleep when the patient is withinthe S1 or S2 sleep states. While monitoring physiological parameters(188) to determine whether patient 12 is asleep (190), IMD 18 maycontinue to monitor the posture and/or activity level of patient 12(182) to confirm that patient 12 is still attempting to fall asleep(184).

When IMD 18 determines that patient 12 is asleep, e.g., by analysis ofthe various parameters contemplated herein, IMD 18 will identify thetime that patient 12 fell asleep (192). While patient 12 is sleeping,IMD 18 will continue to monitor physiological parameters of patient 12(194). As discussed above, IMD 18 may identify the occurrence ofarousals and/or apneas based on the monitored physiological parameters(196). Further, IMD 18 may identify the time that patient 12 transitionsbetween sleep states, e.g., REM, S1, S2, S3, and S4, occur based on themonitored physiological parameters, such as by analysis of the EEGsignal using circuit 68 discussed above with reference to FIG. 4 (196).

Additionally, while patient 12 is sleeping, IMD 122 monitorsphysiological parameters of patient 12 (194) to determine whetherpatient 12 has woken up (198). When IMD 18 determines that patient 12 isawake, IMD 18 identifies the time that patient 12 awoke (200), anddetermines sleep quality metric values based on the informationcollected while patient 12 was asleep (202).

For example, one sleep quality metric value IMD 18 may calculate issleep efficiency, which IMD 18 may calculate as a percentage of timeduring which patient 12 is attempting to sleep that patient 12 isactually asleep. IMD 18 may determine a first amount of time between thetime IMD 18 identified that patient 12 fell asleep and the time IMD 18identified that patient 12 awoke. IMD 18 may also determine a secondamount of time between the time IMD 18 identified that patient 12 beganattempting to fall asleep and the time IMD 18 identified that patient 12awoke. To calculate the sleep efficiency, IMD 18 may divide the firsttime by the second time.

Another sleep quality metric value that IMD 18 may calculate is sleeplatency, which IMD 18 may calculate as the amount of time between thetime IMD 18 identified that patient 12 was attempting to fall asleep andthe time IMD 18 identified that patient 12 fell asleep. Other sleepquality metrics with values determined by IMD 18 based on theinformation collected by IMD 18 in the illustrated example include:total time sleeping per day, at night, and during daytime hours; numberof apnea and arousal events per occurrence of sleep; and amount of timespent in the various sleep states, e.g., one or both of the S3 and S4sleep states. IMD 18 may store the determined values as sleep qualitymetric values 158 within memory 54 or 148.

IMD 18 may perform the example method illustrated in FIG. 9continuously, e.g., may monitor to identify when patient 12 isattempting to sleep and asleep any time of day, each day. In otherembodiments, IMD 18 may only perform the method during evening hoursand/or once every N days to conserve battery and memory resources.Further, in some embodiments, IMD 18 may only perform the method inresponse to receiving a command from patient 12 or a clinician viaprogrammer 28 or one of programmers 128, 134. For example, patient 12may direct IMD 18 to collect sleep quality information at times when thepatient believes that his or her sleep quality is low or therapy isineffective.

FIG. 10 is a flow diagram illustrating an example method for associatingsleep quality information with therapy parameter sets 152 that may beemployed by and IMDs 18, 32 or 122. IMD 18 will be used herein as anexample. IMD 18 determines a value of a sleep quality metric accordingto any of the techniques described above (204). IMD 18 also identifiesthe current therapy parameter set, e.g., the therapy parameter set 152used by IMD 18 to control delivery of therapy when patient 12 was asleep(206), and associates the newly determined value with the currenttherapy parameter set 152.

Among sleep quality metric values 158 within memory 54, IMD 18 stores arepresentative value of the sleep quality metric, e.g., a mean or medianvalue, for each of the plurality of therapy parameter sets 152. IMD 18updates the representative values for the current therapy parameter setbased on the newly determined value of the sleep quality metric. Forexample, a newly determined sleep efficiency value may be used todetermine a new average sleep efficiency value for the current therapyparameter set 152.

FIG. 11 is a block diagram further illustrating clinician programmer210. Clinician programmer 210 may be an embodiment of any programmers 28or 128. A clinician may interact with a processor 212 via a userinterface 214 in order to program therapy for patient 12. Further,processor 212 may receive sleep quality metric values 158 from IMD 122via a telemetry circuit 216, and may generate sleep quality informationfor presentation to the clinician via user interface 214. User interface214 may include a display and keypad, such as display 130 and keypad 132of programmer 128, and may also include a touch screen or peripheralpointing devices as described above. Processor 212 may include amicroprocessor, a controller, a DSP, an ASIC, an FPGA, discrete logiccircuitry, or the like.

Clinician programmer 210 also includes a memory 218. Memory 218 mayinclude program instructions that, when executed by processor 212, causeclinician programmer 210 to perform the functions ascribed to clinicianprogrammers 28 or 128 herein. Memory 218 may include any volatile,non-volatile, fixed, removable, magnetic, optical, or electrical media,such as a RAM, ROM, CD-ROM, hard disk, removable magnetic disk, memorycards or sticks, NVRAM, EEPROM, flash memory, and the like.

FIG. 12 is a flow diagram illustrating an example method for presentingsleep quality information to a clinician that may be employed byclinician programmer 210, e.g., programmers 28 or 128. Clinicianprogrammer 210 receives sleep quality metric values 158 from IMD 122,e.g., via telemetry circuit 216 (220). The sleep quality metric values158 may be daily values, or mean or median values determined overgreater periods of time, e.g., weeks or months.

Clinician programmer 210 may simply present the values to the clinicianvia a display, in any form, such as a table of average values, orclinician programmer 210 may generate a graphical representation of thesleep quality metric values (222). For example, clinician programmer 210may generate a trend diagram illustrating sleep quality metric values158 over time, or a histogram, pie chart, or other graphic illustrationof percentages of sleep quality metric values 158 collected by IMD 122that were within ranges. Where clinician programmer 210 generates agraphical representation of the sleep quality metric values 158,clinician programmer 210 presents the graphical representation to theclinician via the display (224).

FIG. 13 illustrates an example list 226 of therapy parameter sets andassociated sleep quality metric values that may be presented to aclinician by clinician programmer 210. Each row of example list 226includes an identification of one of therapy parameter sets 152, theparameters of the set, and a representative value for one or more sleepquality metrics associated with the identified therapy parameter set,such as sleep efficiency, sleep latency, or both. The example list 226includes representative values for sleep efficiency, sleep latency, and“deep sleep,” e.g., the average amount of time per night spent in eitherof the S3 and S4 sleep states.

FIG. 14 is a flow diagram illustrating an example method for displayinga list 226 of therapy parameter sets and associated sleep qualityinformation that may be employed by clinician programmer 210. Accordingto the example method, clinician programmer 210 receives informationidentifying the plurality of therapy parameter sets 152 stored in memory148 of IMD 122 (or memory 54 of IMD 18, 32), and one or morerepresentative sleep quality metric values associated with each of thetherapy parameter sets (228). Clinician programmer 210 generates a list226 of the therapy parameter sets 152 and any associated representativesleep quality metric values (230), and orders the list according to aselected sleep quality metric (232). For example, in the example list226 illustrated in FIG. 13, the clinician may select whether list 226should be ordered according to sleep efficiency or sleep latency viauser interface 214 of clinician programmer 210.

FIG. 15 is a block diagram further illustrating patient programmer 234.Patient programmer 234 may be an embodiment of programmers 28 or 134.Patient 12 may interact with a processor 236 via a user interface 238 inorder to control delivery of therapy, i.e., select or adjust one or moreof therapy parameter sets 152 stored by IMD 122 (or IMDs 18 and 32).Processor 236 may also receive sleep quality metric values 158 from IMD122 via a telemetry circuit 240, and may provide messages related tosleep quality to patient 12 via user interface 238 based on the receivedvalues. User interface 238 may include a display and keypad, such asdisplay 28 and keypad 30 of patient programmer 134, and may also includea touch screen or peripheral pointing devices as described above.

In some embodiments, processor 236 may determine whether to provide amessage related to sleep quality to patient 12 based on the receivedsleep quality metric values. For example, processor 236 may periodicallyreceive sleep quality metric values 158 from IMD 122 when placed intelecommunicative communication with IMD 122 by patient 12, e.g., fortherapy selection or adjustment. Processor 236 may compare these valuesto one or more thresholds 242 stored in a memory 244 to determinewhether the quality of the patient's sleep is poor enough to warrant amessage.

Processor 236 may present messages to patient 12 as text via display,and/or as audio via speakers included as part of user interface 238. Themessage may, for example, direct patient 12 to see a physician, increasetherapy intensity before sleeping, or select a different therapyparameter set before sleeping than the patient had typically selectedpreviously. In some embodiments, the message may indicate the quality ofsleep to patient 12 to, for example, provide patient 12 with anobjective indication of whether his or her sleep quality is good,adequate, or poor. Further, in some embodiments, processor 236 may, likeclinician programmer 210, receive representative sleep quality metricvalues. In such embodiments, processor 236 may identify a particular oneor more of therapy parameter sets 152 to recommend to patient 12 basedon representative sleep quality metric values associated with thoseprograms.

Processor 236 may include a microprocessor, a controller, a DSP, anASIC, an FPGA, discrete logic circuitry, or the like. Memory 244 mayalso include program instructions that, when executed by processor 236,cause patient programmer 234 to perform the functions ascribed topatient programmer 234 herein. Memory 244 may include any volatile,non-volatile, fixed, removable, magnetic, optical, or electrical media,such as a RAM, ROM, CD-ROM, hard disk, removable magnetic disk, memorycards or sticks, NVRAM, EEPROM, flash memory, and the like.

FIG. 16 is a flow diagram illustrating an example method for presentinga sleep quality message to patient 12 that may be employed by patientprogrammer 234, e.g., programmers 28 or 134. According to theillustrated example method, patient programmer 234 receives a sleepquality metric value from IMD 122 (or IMDs 18 or 32) (246), and comparesthe value to a threshold value 242 (248). Patient programmer 234determines whether the comparison indicates poor sleep quality (250). Ifthe comparison indicates that the quality of sleep experienced bypatient 12 is poor, patient programmer 234 presents a message related tosleep quality to patient 12 (252).

Various embodiments of the invention have been described. However, oneskilled in the art will recognize that various modifications may be madeto the described embodiments without departing from the scope of theinvention. For example, although described herein primarily in thecontext of treatment of pain with an implantable neurostimulator, theinvention is not so limited. The invention may be embodied in anyimplantable medical device, such as a cardiac pacemaker, an implantablepump, or an implantable monitor that does not itself deliver a therapyto the patient. Further, the invention may be implemented via anexternal, e.g., non-implantable, medical device.

As discussed above, the ability of a patient to experience qualitysleep, e.g., the extent to which the patient able to achieve adequateperiods of undisturbed sleep in deeper, more restful sleep states, maybe negatively impacted by any of a variety of ailments or symptoms.Accordingly, the sleep quality of a patient may reflect the progression,status, or severity of the ailment or symptom. Further, the sleepquality of the patient may reflect the efficacy of a particular therapyor therapy parameter set in treating the ailment or symptom. In otherwords, it may generally be the case that the more efficacious a therapyor therapy parameter set is, the higher quality of sleep the patientwill experience.

As discussed above, in accordance with the invention, sleep qualitymetrics may be monitored, and used to evaluate the status, progressionor severity of an ailment or symptom, or the efficacy of therapies ortherapy parameter sets used to treat the ailment or symptom. As anexample, epileptic seizures may disturb a patient's sleep, and poorsleep may lead to more frequent or severe seizures.

In some embodiments, systems according to the invention may include anyof a variety of medical devices that deliver any of a variety oftherapies to treat epilepsy, such as DBS, or one or more drugs, orcooling therapy. Systems may use the techniques of the inventiondescribed above to determine sleep quality metrics for the patient andevaluate such therapies, e.g., by associating sleep quality metrics withtherapy parameter sets for delivery of such therapies. Systems accordingto the invention may thereby evaluate the extent to which a therapy ortherapy parameter set is alleviating epilepsy by evaluating the extentto which the therapy or therapy parameter set improves sleep quality forthe patient.

As another example, chronic pain may cause a patient to have difficultyfalling asleep, experience arousals during sleep, or have difficultyexperiencing deeper sleep states. Systems according to the invention maymonitor sleep quality metrics to evaluate the extent to which thepatient is experiencing pain.

In some embodiments, systems according to the invention may include anyof a variety of medical devices that deliver any of a variety oftherapies to treat chronic pain, such as SCS, DBS, cranial nervestimulation, peripheral nerve stimulation, or one or more drugs. Systemsmay use the techniques of the invention described above to determinesleep quality metrics for the patient and evaluate such therapies, e.g.,by associating sleep quality metrics with therapy parameter sets fordelivery of such therapies. Systems according to the invention maythereby evaluate the extent to which a therapy or therapy parameter setis alleviating chronic pain by evaluating the extent to which thetherapy or therapy parameter set improves sleep quality for the patient.

As another example, psychological disorders may cause a patient toexperience low sleep quality. Accordingly, embodiments of the inventionmay determine sleep quality metrics to track the status or progressionof a psychological disorder, such as depression, mania, bipolardisorder, or obsessive-compulsive disorder. Further, systems accordingto the invention may include any of a variety of medical devices thatdeliver any of a variety of therapies to treat a psychological disorder,such as DBS, cranial nerve stimulation, peripheral nerve stimulation,vagal nerve stimulation, or one or more drugs. Systems may use thetechniques of the invention described above to associate sleep qualitymetrics with the therapies or therapy parameter sets for delivery ofsuch therapies, and thereby evaluate the extent to which a therapy ortherapy parameter set is alleviating the psychological disorder byevaluating the extent to which the therapy parameter set improves thesleep quality of the patient.

Movement disorders, such as tremor, Parkinson's disease, multiplesclerosis, and spasticity may also affect the sleep quality experiencedby a patient. The uncontrolled movements, e.g., tremor or shaking,associated such disorders, particularly in the limbs, may cause apatient to experience disturbed sleep. Accordingly, systems according tothe invention may monitor sleep quality metrics to determine the stateor progression of a movement disorder.

Further, systems according to the invention may include any of a varietyof medical devices that deliver any of a variety of therapies to treatmovement disorders, such as DBS, cortical stimulation, or one or moredrugs. Baclofen, which may or may not be intrathecally delivered, is anexample of a drug that may be delivered to treat movement disorders.Systems may use the techniques of the invention described above toassociate sleep quality metrics with therapies or therapy parameter setsfor delivery of such therapies. In this manner, such systems may allow auser to evaluate the extent to which a therapy or therapy parameter setis alleviating the movement disorder by evaluating the extent to whichthe therapy parameter set improves the sleep quality experienced by thepatient.

Additionally, the invention is not limited to embodiments in which aprogramming device receives information from the medical device, orpresents information to a user. Other computing devices, such ashandheld computers, desktop computers, workstations, or servers mayreceive information from the medical device and present information to auser as described herein with reference to programmers 28, 128 or 134. Acomputing device, such as a server, may receive information from themedical device and present information to a user via a network, such asa local area network (LAN), wide area network (WAN), or the Internet. Insome embodiments, the medical device is an external medical device, andmay itself include a display to present information to a user.

As another example, the invention may be embodied in a trialneurostimulator, which is coupled to percutaneous leads implanted withinthe patient to determine whether the patient is a candidate forneurostimulation, and to evaluate prospective neurostimulation therapyparameter sets. Similarly, the invention may be embodied in a trial drugpump, which is coupled to a percutaneous catheter implanted within thepatient to determine whether the patient is a candidate for animplantable pump, and to evaluate prospective therapeutic agent deliveryparameter sets. Sleep quality metric values collected by the trialneurostimulator or pump may be used by a clinician to evaluate theprospective therapy parameter sets, and select parameter sets for use bythe later implanted non-trial neurostimulator or pump. In particular, atrial neurostimulator or pump may determine representative values of oneor more sleep quality metrics for each of a plurality of prospectivetherapy parameter sets, and a computing device, such as a clinicianprogrammer, may present a list of prospective parameter sets andassociated representative values to a clinician. The clinician may usethe list to identify potentially efficacious parameter sets, and mayprogram a permanent implantable neurostimulator or pump for the patientwith the identified parameter sets.

Further, the invention is not limited to embodiments in which animplantable or external medical device that delivers therapy to apatient determines sleep quality metric values. Instead a medical deviceaccording to the invention may record values for one or morephysiological parameters, and provide the physiological parameter valuesto a computing device, such as one or both of programmers 28, 128 or134. In such embodiments, the computing device, and more particularly aprocessor of the computing device, e.g., processors 212, 236, employsany of the techniques described herein with reference to IMDs 18, 32,and 122 in order to determine sleep quality metric values based on thephysiological parameter values received from the medical device. Thecomputing device may receive physiological parameter values from themedical device in real time, or may monitor physiological parameters ofthe patient by receiving and analyzing physiological parameter valuesrecorded by the medical device over a period of time. In someembodiments, in addition to physiological parameter values, the medicaldevice provides the computing device information identifying times atwhich the patient indicated that he or she was attempting to fallasleep, which the computing device may use to determine one or moresleep quality metric values as described herein.

In some embodiments, the medical device may associate recordedphysiological parameter values with current therapy parameter sets. Themedical device may provide information indicating the associations ofrecorded physiological parameter values and therapy parameter sets tothe computing device, e.g., programmer 28, 128 or 134. The computingdevice may determine sleep quality metric values and representativesleep quality metric values for each of the plurality of therapyparameter sets based on the physiological parameter values associatedwith the therapy parameter sets, as described herein with reference toIMDs 18, 32, and 122.

Additionally, the invention is not limited to embodiments in which thetherapy delivering medical device monitors the physiological parametersof the patient described herein. In some embodiments, a separatemonitoring device monitors values of one or more physiologicalparameters of the patient instead of, or in addition to, a therapydelivering medical device. The monitor may include a processor 146 andmemory 148, and may be coupled to sensors 142, as illustrated above withreference to IMD 122 and FIGS. 6 and 7. The monitor may identify sleepquality metric values based on the values of the monitored physiologicalparameter values, or may transmit the physiological parameter values toa computing device for determination of the sleep quality metric values.In some embodiments, an external computing device, such as a programmingdevice, may incorporate the monitor.

FIG. 17 is a conceptual diagram illustrating a monitor 254 that monitorsvalues of one or more physiological parameters of the patient insteadof, or in addition to, a therapy delivering medical device such as anyof IMDs 18, 32 or 122. In the illustrated example, monitor 254 isconfigured to be attached to or otherwise carried by a belt 256, and maythereby be worn by patient 12. FIG. 6 also illustrates various sensors142 that may be coupled to monitor 254 by leads, wires, cables, orwireless connections, such as EEG electrodes 258A-C placed on the scalpof patient 12, a plurality of EOG electrodes 260A and 260B placedproximate to the eyes of patient 12, and one or more EMG electrodes 262placed on the chin or jaw the patient. The number and positions ofelectrodes 258, 260 and 262 illustrated in FIG. 17 are merely exemplary.For example, although only three EEG electrodes 258 are illustrated inFIG. 17, an array of between 16 and 25 EEG electrodes 258 may be placedon the scalp of patient 12, as is known in the art. EEG electrodes 258may be individually placed on patient 12, or integrated within a cap orhair net worn by the patient.

In the illustrated example, patient 12 wears an ECG belt 264. ECG belt264 incorporates a plurality of electrodes for sensing the electricalactivity of the heart of patient 12. The heart rate and, in someembodiments, ECG morphology of patient 12 may monitored by monitor 254based on the signal provided by ECG belt 264. Examples of suitable belts264 for sensing the heart rate of patient 12 are the “M” and “F” heartrate monitor models commercially available from Polar Electro. In someembodiments, instead of belt 264, patient 12 may wear a plurality of ECGelectrodes attached, e.g., via adhesive patches, at various locations onthe chest of the patient, as is known in the art. An ECG signal derivedfrom the signals sensed by such an array of electrodes may enable bothheart rate and ECG morphology monitoring, as is known in the art.

As shown in FIG. 17, patient 12 may also wear a respiration belt 266that outputs a signal that varies as a function of respiration of thepatient. Respiration belt 266 may be a plethysmograpy belt, and thesignal output by respiration belt 266 may vary as a function of thechanges is the thoracic or abdominal circumference of patient 12 thataccompany breathing by the patient. An example of a suitable belt 266 isthe TSD201 Respiratory Effort Transducer commercially available fromBiopac Systems, Inc. Alternatively, respiration belt 266 may incorporateor be replaced by a plurality of electrodes that direct an electricalsignal through the thorax of the patient, and circuitry to sense theimpedance of the thorax, which varies as a function of respiration ofthe patient, based on the signal. In some embodiments, ECG andrespiration belts 264 and 266 may be a common belt worn by patient 12,and the relative locations of belts 264 and 266 depicted in FIG. 17 aremerely exemplary.

In the example illustrated by FIG. 17, patient 12 also wears atransducer 268 that outputs a signal as a function of the oxygensaturation of the blood of patient 12. Transducer 268 may be an infraredtransducer. Transducer 268 may be located on one of the fingers orearlobes of patient 12. Sensors 142 coupled to monitor 254 mayadditionally or alternatively include any of the variety of sensorsdescribed above that monitor any one or more of activity level, posture,heart rate, ECG morphology, respiration rate, respiratory volume, bloodpressure, blood oxygen saturation, partial pressure of oxygen withinblood, partial pressure of oxygen within cerebrospinal fluid, muscularactivity and tone, core temperature, subcutaneous temperature, arterialblood flow, brain electrical activity, eye motion, and galvanic skinresponse.

The invention may also be embodied as a computer-readable medium thatincludes instructions to cause a processor to perform any of the methodsdescribed herein. These and other embodiments are within the scope ofthe following claims.

The invention claimed is:
 1. A method comprising: delivering a therapyfrom a medical device to a patient to treat a non-respiratoryneurological disorder of the patient via a lead positioned within abrain of the patient; sensing electrical activity of the brain of thepatient, during the therapy of the patient, with at least one electrodeof the lead positioned within the brain of the patient, the at least oneelectrode being in communication with the medical device; determiningsleep states of the patient based on the sensed electrical activity ofthe brain of the patient, wherein determining the sleep states of thepatient includes comparing the relative power levels of differentfrequency bands of the sensed electrical activity of the brain of thepatient; determining values of a sleep quality metric based on the sleepstates of the patient determined based on the electrical activity of thebrain of the patient; and providing the sleep quality metric values to auser for evaluation of the therapy.
 2. The method of claim 1, whereindetermining values of the sleep quality metric is further based on atleast one of activity level, posture, heart rate, electrocardiogrammorphology, or core temperature.
 3. The method of claim 1, wherein thesleep quality metric comprises at least one of sleep efficiency or sleeplatency.
 4. The method of claim 1, wherein determining values of thesleep quality metric comprises at least one of determining an amount oftime that the patient is asleep during a period, or identifying a numberof arousal events during a period of sleep.
 5. The method of claim 1,wherein determining values of the sleep quality metric comprisesdetermining an amount of time that the patient was within at least oneof an S3 sleep state or an S4 sleep state.
 6. The method of claim 1,wherein determining values of the sleep quality metric comprises:determining values of each of a plurality of sleep quality metrics; anddetermining values of an overall sleep quality metric based on thevalues of the plurality of sleep quality metrics.
 7. The method of claim1, wherein providing the sleep quality metric values to a user comprisespresenting a graphical representation of the sleep quality metric. 8.The method of claim 7, wherein presenting a graphical representationcomprises presenting at least one of a trend diagram, a histogram, or apie chart based on the plurality of values of the sleep quality metric.9. The method of claim 1, further comprising presenting a messagerelated to sleep quality to the patient via a patient programmer basedon the sleep quality metric values.
 10. The method of claim 1, whereindelivering therapy comprises delivering therapy according to a pluralityof therapy parameter sets, the method further comprising: associatingeach of the determined sleep quality metric values with the one of thetherapy parameter sets according to which the therapy was delivered whenthe electrical activity of the brain based on which the sleep qualitymetric value was determined was sensed; and for each of a plurality oftherapy parameter sets, determining a representative value of the sleepquality metric based on the values of the sleep quality metricassociated with the therapy parameter set.
 11. The method of claim 10,further comprising: presenting a list of the therapy parameter sets andthe associated representative values to a user; and ordering the list oftherapy parameter sets according to the associated representativevalues.
 12. The method of claim 10, further comprising selecting one ofthe therapy parameter sets for delivery of therapy based on theassociated sleep quality metric values.
 13. The method of claim 1,wherein delivering therapy comprises delivering therapy according to aplurality of therapy parameter sets, the method further comprising:determining a plurality of values over time for each of a plurality ofsleep quality metrics based on the electrical activity of the brain ofthe patient; associating each of the determined sleep quality metricvalues with the one of the therapy parameter sets according to which thetherapy was delivered when the electrical activity of the brain based onwhich the sleep quality metric value was determined was sensed; and foreach of the therapy parameter sets, determining a representative valuefor each of the sleep quality metrics based on the values of that sleepquality metric associated with the therapy parameter set.
 14. The methodof claim 13, further comprising: presenting a list of the therapyparameter sets and the associated representative values to a user; andordering the list of therapy parameter sets according to therepresentative values of a user-selected one of the sleep qualitymetrics.
 15. The method of claim 1, further comprising modifying thetherapy based on the sleep quality metric values.
 16. The method ofclaim 1, wherein delivering a therapy comprises delivering at least oneof an epilepsy therapy, a movement disorder therapy, or a psychologicaldisorder therapy.
 17. The method of claim 1, wherein delivering atherapy comprises delivering deep brain stimulation (DBS).
 18. Themethod of claim 1, wherein the sleep states of the patient is determinedbased upon the frequency band that has the highest relative power level.19. The method of claim 1, further comprising selecting differenttherapy parameter sets for the therapy from the medical device based onthe sleep quality metric values.
 20. The method of claim 1, wherein oneof the frequency bands includes frequencies between 30 Hz andapproximately 50 Hz.
 21. A medical system comprising: a medical deviceconfigured to deliver a therapy to a patient to treat a non-respiratoryneurological disorder of the patient via at least one electrodeconfigured to be positioned within a brain of the patient; the at leastone electrode, the at least one electrode being in communication withthe medical device; and a processor that determines sleep states of thepatient based on electrical activity of the brain of the patient,wherein determining the sleep states of the patient includes comparingthe relative power levels of different frequency bands of the sensedelectrical activity of the brain of the patient, determines values of asleep quality metric based on the sleep states of the patient determinedbased on the electrical activity of the brain of the patient, andprovides the sleep quality metric values to a user for evaluation of thetherapy.
 22. The medical system of claim 21, wherein the processordetermines values of the sleep quality metric based on the electricalactivity of the brain of the patient, and at least one of activitylevel, posture, heart rate, electrocardiogram morphology, or coretemperature.
 23. The medical system of claim 21, further comprising atleast one lead configured to be coupled to the medical device andimplanted in the brain of the patient, the at least one lead includingthe at least one electrode.
 24. The medical system of claim 21, whereinthe sleep quality metric comprises at least one of sleep efficiency orsleep latency.
 25. The medical system of claim 21, wherein the sleepquality metric comprises at least one of an amount of time that thepatient is asleep during a period, or a number of arousal events duringa period of sleep.
 26. The medical system of claim 21, wherein the sleepquality metric comprises an amount of time that the patient was withinat least one of an S3 sleep state or an S4 sleep state.
 27. The medicalsystem of claim 21, wherein the processor determines values of each of aplurality of sleep quality metrics, and determines values of an overallsleep quality metric based on the values of the plurality of sleepquality metrics.
 28. The medical system of claim 21, further comprisinga user interface that presents a graphical representation of the valuesof the sleep quality metric.
 29. The medical system of claim 28, whereinthe graphical representation comprises at least one of a trend diagram,a histogram, or a pie chart.
 30. The medical system of claim 21, furthercomprising a user interface that provides a message related to sleepquality to the patient based on the sleep quality metric values.
 31. Themedical system of claim 21, wherein the medical device delivers therapyaccording to a plurality of therapy parameter sets, and wherein theprocessor associates each of the determined sleep quality metric valueswith the one of the therapy parameter sets according to which thetherapy was delivered when the electrical activity of the brain based onwhich the sleep quality metric value was determined was sensed, and, foreach of a plurality of therapy parameter sets, determines arepresentative value of the sleep quality metric based on the values ofthe sleep quality metric associated with the therapy parameter set. 32.The medical system of claim 31, further comprising a user interface thatpresents a list of the therapy parameter sets ordered according to theassociated representative values.
 33. The medical system of claim 31,wherein the processor selects one of the therapy parameter sets fordelivery of therapy by the medical device based on the associated sleepquality metric values.
 34. The medical system of claim 21, wherein themedical device delivers therapy according to a plurality of therapyparameter sets, and wherein the processor determines a plurality ofvalues over time for each of a plurality of sleep quality metrics basedon the electrical activity of the brain of the patient, associates eachof the determined sleep quality metric values with the one of thetherapy parameter sets according to which the therapy was delivered whenthe electrical activity of the brain based on which the sleep qualitymetric value was determined was sensed, and, for each of the therapyparameter sets, determines a representative value for each of the sleepquality metrics based on the values of that sleep quality metricassociated with the therapy parameter set.
 35. The medical system ofclaim 34, further comprising a user interface that presents a list ofthe therapy parameter sets and the associated representative valuesordered according to the representative values of a user-selected one ofthe sleep quality metrics.
 36. The medical system of claim 21, whereinthe processor modifies the therapy based on the sleep quality metricvalues.
 37. The medical system of claim 21, wherein the medical devicedelivers at least one of an epilepsy therapy, a movement disordertherapy, or a psychological disorder therapy.
 38. The medical system ofclaim 21, wherein the medical device is configured to be coupled to atleast one lead implanted within the brain and configured to deliver deepbrain stimulation (DBS) via the at least one lead, wherein the at leastone lead includes the at least one electrode.
 39. The medical system ofclaim 38, wherein the medical device is configured for implantationbeneath a scalp of the patient.
 40. The medical system of claim 21,wherein the processor is housed within the medical device.
 41. Themedical system of claim 21, wherein the processor determines the sleepstates of the patient based upon the frequency band that has the highestrelative power level.
 42. The medical system of claim 21, wherein theprocessor selects different therapy parameter sets for the therapy fromthe medical device based on the sleep quality metric values.
 43. Themedical system of claim 21, wherein one of the frequency bands includesfrequencies between 30 Hz and approximately 50 Hz.
 44. A methodcomprising: delivering deep brain stimulation (DBS) therapy from amedical device to a patient via a lead implanted in a brain of thepatient; sensing electrical activity of the brain of the patient withthe lead in conjunction with the DBS therapy; determining sleep statesof the patient based on the sensed electrical activity of the brain ofthe patient, wherein determining the sleep states of the patientincludes comparing the relative power levels of different frequencybands of the sensed electrical activity of the brain of the patient;determining values of a sleep quality metric based on the sleep statesof the patient determined based on the electrical activity of the brainof the patient; and providing the sleep quality metric values to a userfor evaluation of the DBS therapy.
 45. The method of claim 44, whereindetermining values of the sleep quality metric is further based on atleast one of activity level, posture, heart rate, electrocardiogrammorphology, or core temperature.
 46. The method of claim 44, whereindelivering DBS therapy comprises delivering DBS therapy according to aplurality of therapy parameter sets, the method further comprising:associating each of the determined sleep quality metric values with theone of the therapy parameter sets according to which the therapy wasdelivered when the electrical activity of the brain based on which thesleep quality metric value was determined was sensed; and for each of aplurality of therapy parameter sets, determining a representative valueof the sleep quality metric based on the values of the sleep qualitymetric associated with the therapy parameter set.
 47. The method ofclaim 44, wherein delivering DBS therapy comprises delivering DBS totreat at least one of epilepsy, a movement disorder, or a psychologicaldisorder.
 48. The method of claim 44, wherein the sleep states of thepatient is determined based upon the frequency band that has the highestrelative power level.
 49. The method of claim 44, further comprisingselecting different therapy parameter sets for the DBS therapy from themedical device based on the sleep quality metric values.
 50. The methodof claim 44, wherein one of the frequency bands includes frequenciesbetween 30 Hz and approximately 50 Hz.
 51. A medical system comprising alead configured to be implanted in a brain of a patient; a medicaldevice configured to be coupled to the lead and configured to deliverdeep brain stimulation (DBS) therapy to the patient via the lead; and aprocessor that determines sleep states of the patient based onelectrical activity of the brain of the patient sensed with the lead,wherein determining the sleep states of the patient includes comparingthe relative power levels of different frequency bands of the sensedelectrical activity of the brain of the patient, determines values of asleep quality metric based on the sleep states of the patient determinedbased on the electrical activity of the brain of the patient sensed withthe lead during treatment of the patient with the DBS therapy, andprovides the sleep quality metric values to a user for evaluation of theDBS therapy.
 52. The medical system of claim 51, wherein the processordetermines values of the sleep quality metric further based on at leastone of activity level, posture, heart rate, electrocardiogrammorphology, or core temperature.
 53. The medical system of claim 51,wherein the medical device delivers DBS therapy according to a pluralityof therapy parameter sets, and the processor associates each of thedetermined sleep quality metric values with the one of the therapyparameter sets according to which the therapy was delivered when theelectrical activity of the brain based on which the sleep quality metricvalue was determined was sensed, and, for each of a plurality of therapyparameter sets, determines a representative value of the sleep qualitymetric based on the values of the sleep quality metric associated withthe therapy parameter set.
 54. The medical system of claim 51, whereinthe medical device delivers DBS to treat at least one of epilepsy, amovement disorder, or a psychological disorder.
 55. The medical systemof claim 51, wherein the medical device is configured for implantationbeneath a scalp of the patient.
 56. The medical system of claim 51,wherein the processor determines the sleep states of the patient basedupon the frequency band that has the highest relative power level. 57.The medical system of claim 51, wherein the processor selects differenttherapy parameter sets for the DBS therapy from the medical device basedon the sleep quality metric values.
 58. The medical system of claim 51,wherein one of the frequency bands includes frequencies between 30 Hzand approximately 50 Hz.